Slide 1

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 2

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 3

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 4

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 5

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 6

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 7

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 8

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 9

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 10

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 11

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 12

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 13

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 14

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 15

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 16

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 17

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 18

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 19

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 20

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 21

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 22

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 23

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 24

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 25

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 26

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 27

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 28

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 29

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 30

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 31

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 32

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 33

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 34

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 35

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 36

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 37

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 38

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 39

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 40

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 41

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 42

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 43

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 44

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 45

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 46

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 47

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 48

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 49

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 50

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 51

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 52

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 53

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 54

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 55

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 56

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 57

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 58

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 59

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 60

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 61

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 62

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 63

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

Online Workshop Evaluation
Forms will be sent shortly
after the workshop. Please
take the time to complete this
short online survey.


Slide 64

Putting Your Research
Ideas Into Practice
FH Health Research
Intelligence Unit

Who we are:
Dr. Peter Hill (604-587-4641), Vice-President,
Academic, Research, and Clinical System Redesign.

Susan Chunick (604-587-4681), Director, Research
Administration and Development (RAD).

Rosa Haywood (604-587-4436), RAD
administrative assistant.

Michael Wasdell (604-587-4637), Grant Facilitator-Writer.

Rae Spiwak (604-587-4438), Epidemiologist.

RESEARCH ETHICS BOARD
STATUS REPORT 01 October 2007
Total Studies 388
(From 2005 September 01 to Date)
ACTIVE STUDIES =204
Type of Ethics Review for
ACTIVE Studies

PENDING APPROVAL = 18
Funding for ACTIVE Studies

80

79

60

110
F ull B o a rd

94

E xpe dit e d

72

40

38

20
0

Sponsor

Grant

5

2

8
Grant-Aid

Unfunded

FH Internal

Other

Active Studies by Department Area
Addiction Services

1

Finance

1

Neurology

3

Pharmacy

17

Administration

1

Food & Nutrition

1

Nursing

5

Prevention/Promotion

1

Anaesthesia

1

Health Services

2

Nutrition

1

Professional Practice

4

Cardiology

27

Home Health

1

Obstetrics

6

Psychiatry

18

Community Site

1

Infection Control

1

Occupational Therapy

2

Public Health Prevention

1

Critical Care (ICU)

13

Information
Management

1

Oncology

5

Residency Facility

1

Decision Support Service

1

Internal Medicine

2

Orthopaedics

15

Respiratory

1

Diversity Services

1

Laboratory-Cytology

1

Palliative Care

2

Social Work

3

Elder Research

3

Medicine

6

Pathology

1

Surgery

2

Emergency

7

Mental Health

6

Paediatrics

2

Systems
Analysis/Performance

1

Ethics

1

Multiple Sclerosis

8

People Service

1

Workplace Health

7

Family Medicine

3

Nephrology

5

Perinatal

2

Non FH Research

8

FH Health Research Intelligence
Unit
How can we help?


Grant Facilitator-Writer
 Conducting a search for funding opportunities
 Automatic notification of new funding sources and deadlines
 Identifying a research team
 Preparing letters of intent
 Identifying resources required for conducting research
 Formulating the research budget
 Writing the grant application in collaboration with
researchers
 Understanding FH and funding agency requirements
regarding preparation of specific documents

FH Health Research Intelligence
Unit
How can we help?











Epidemiologist
Specifying the research goal,
objectives and hypothesis
Identifying measurable
outcomes
Specifying the variables for
analysis
Identifying sources of data
Developing data collection
tools for quantitative or
qualitative studies
Developing the statistical
analysis plan
Analyzing the data
Understanding how to use
statistical software, such as
SPSS

Workshop Activities
 What

is research?
 Research process
 Introductory statistics for practical use
 Assignment of work group
 Group work:
•
•
•
•

Practical Exercise for identifying:
Research Goals
Statistical Tests
Levels of Data

 Sharing

of group work

What is research?
“….the systematic process of collecting and
analyzing information (data) in order to
increase our understanding of the phenomenon
about which we are concerned or interested.”
(Leedy and Ormrod, 2001)

Research Types










CONCLUSION-ORIENTED RESEARCH
Epidemiological
Experimental
Effectiveness/Evaluation
Theory Generation
Health Services
DECISION-ORIENTED RESEARCH
Technical Analyses – systematic reviews
Policy Analysis & Research
Evaluation Research

Research Uses
Demand for Research Servcies
Number of Consultation Requests, Consultations Provided and Completed Consultations
350

Grant Facilitator (March 06 - October 07)
300

Epidemiologist (August 05 - October 07)
290

285
270

250

200
199

194
178

150

100

29% of total
epidemiological consults
involve decision-oriented
research

50

0
# Number of Consutation Requests

# Number of Consultations

# Completed Consultations

Fraser Health
Research Involving Human Subjects
Research involving human subjects is defined as any
systematic investigation (including pilot studies,
exploratory studies, and academic course work
assignments) designed to contribute to generalizable
knowledge.
Generalizable knowledge consists of facts, theories,
principles or relationships, or the accumulation of
information on which they are based, that can be
corroborated by accepted scientific methods of
observation and inference.

Research Characteristics
 1.

Originates with question/problem
 2. Guided by the hypothesis, question or
problem
 3. Follows a specific plan or procedure.
 4. Accepts certain critical assumptions
 5. Requires collection and interpretation of
data
 6. Cyclical in nature

Types of Health Research
 Biomedical

Research
 Clinical Research
 Health Services/Systems Research
 Population Health Research

Health Research
 Biomedical








Research

To understand normal and abnormal
functioning at the molecular, cellular, organ
system and whole body levels.
Includes the development of tools and
techniques and new therapies or devices that
can improve health and quality of life up to the
point where they are tested on human
subjects.
Does not have a diagnostic or therapeutic
orientation.
i.e. Looking at spontaneous mutations in cells.

Health Research
 Clinical








Research

Targets improving the diagnosis and
treatment of disease and injury.
Focus on the health and quality of life of
individuals.
Includes research on animal models of human
disease, clinical trials and other therapeutic
interventions.
i.e. Testing the effectiveness of a new
medication

Health Research
 Health







Services/Systems Research

Multidisciplinary field
Aims to improve the efficiency and
effectiveness of health professionals and the
health care system.
Interventions at the level of practice and
policy.
i.e. Evaluating a new intervention for
improving patient flow.

Health Research


Population Health Research








Studies the impact of social and environmental
factors on the health of populations/subpopulations.
May examine social, cultural, environmental,
occupational, and economic factors that determine
health status.
Research data is used to identify areas where the
health of a population can be improved.
i.e. Statistics Canada Community Health Survey.

Research is a process










1. Generate idea
2. Conduct literature review
3. Refine research question
4. Plan research methodology
5. Create research proposal
6. Apply for funding
7. Apply for ethics approval
8. Collect and analyze data
9. Draw conclusions and relate findings

Conducting Research
Evaluation Guidelines
 Significance and relevance to health
 Knowledge of the field (cited literature)
 Clear, testable hypothesis or central
research problem
 Originality and innovation in concept or
approach
 Feasibility of work plan

Significance and Relevance to Health
Research begins with the identification of a
problem/knowledge gap and formulation of a
research question.




Identifying this problem can be the hardest part of
research.
The problem or question does not have to be
complex.

Where to obtain a research idea






Experience in your area of specialty.
Knowledge of the relevant literature and issues.
Practice guidelines.
Journal editorials and review articles.

Significance and Relevance to Health


Good research proposals
should:
 Address an important
question.
 State the problem clearly and
completely.
 Advance knowledge.



How do you know if your idea is
good?
 Talk to your peers and other
experts in your field.
 Obtain an outside opinion.
 Look to the literature to see if
it has already been studied.

Knowledge of the Field
Sources of Research Literature
 Journal articles – PUBMED, MEDLINE,
EMBASE, etc
 Conference papers - Conference
Proceedings
 Dissertations - Dissertation Abstracts
 Gray literature (reports, working papers,
government documents)

Knowledge of the Field
What to look for
 Systematic Reviews




Meta-analysis




An approach to summarizing the medical literature
where the methods undertaken to conduct the search
are reported so that it may be replicated and updated.
A review that uses quantitative methods to summarize
the results.

Review Articles
 Original Articles

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Phase




Theory Building (creating new theory)
Testing (testing a question or hypothesis)
Theory Extension (adding to existing theory)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Purpose






Exploration
Description
Explanation
Prediction
Hypothesis Testing

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Unit of Analysis
 Individual
 Dyad
 Group
 Organizational Unit
 Industry Segment / Sector
 Community
 Society

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Definition





Population (entire group)
Sampling Frame (how your sample will be
selected)
Sample (sample of entire group)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Time Horizon




Cross- Sectional (snap-shot)
Longitudinal (over time)
Retrospective (past tense)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:

Study Setting





Natural (i.e. Participant observation in natural
setting)
Contrived (i.e. artificial setting)
Researcher Influence (i.e. Randomized
Control Trial)

Clear, Testable Problem/Hypothesis
Factors to consider when developing an
hypothesis or statement of the research
question:
Research Design – depends on the research
question
 Descriptive
 Analytic
 Experimental
 Quasi-Experimental
 Qualitative

Methodology Comparison
Quantitative







Explanation,
prediction
Test theories
Known variables
Larger sample
Standardized
instruments
Deductive

Qualitative







Explanation,
description
Build theories
Unknown variables
Smaller sample
Observations,
interviews
Inductive

Classification of Research
Studies
Observational Studies:
Descriptive Studies:
Research Studies
Observational

Experimental

Focus on describing
populations and describing the
relationship between variables
Analytic Studies:

Descriptive

Analytic

Make inferences about the
population based on a random
sample.
Experimental Studies:
Test relationships between
exposures and outcomes.
Investigator has direct control
over study condition and
exposure status.

Hierarchy of Studies
Type of study is selected
according to the purpose of
research.
Experimental Studies

If you want to control the status
of an exposure or intervention, in
order to examine causality or
effectiveness, choose an
experimental study.

Analytic Studies

If you want to make inferences
about a population based on a
sample, or look at the association
between variables, choose an
analytic study.

Descriptive Studies

If you want to describe a
population, choose a descriptive
study

What about Quasi-Experimental?




Quasi=Almost
Lacks random assignment
Many types:
Pretest Posttest Nonequivalent Group- Both a control group and
an experimental group are compared. But, groups are chosen
and assigned out of convenience (rather than randomization).
Ex. Examining two groups of students. One group signs up for
an educational program, one group does not.
Would measure all of the students’ grades prior to the start of
the program and then again after the program.
Those students who participated would be our treatment
group; those who did not would be our control group.

Clear, Testable Problem/Hypothesis
Method:


Subjects/Patients/Units of Measure






Procedure







Sample size and justification (power, statistical tests)
Inclusion criteria (who will you include?)
Exclusion criteria (who will you exclude?)
Detailed account of how the research design is
executed (methods)
Provides information on timelines and measures
Sufficient information so that it can be replicated

Analysis


Statistical analysis is appropriate for the research
design and will answer the research question

Clear, Testable Problem/Hypothesis
Methodological Considerations
 Objectivity/Bias
 Definitions
 Reliability
 Validity
 Assumptions

 Limitations
 Confounds

Originality and Innovation in Concept or
Approach
Open up a new area
Provide a unifying framework
Resolve a long-standing question
Thoroughly explore an area
Challenge existing knowledge
Experimentally validate a theory
Produce an efficient system
Provide needed empirical data
Derive superior algorithms
Develop new methodology
Develop a new tool

Feasibility of Work Plan


Time
 Human Resources
 Technology
 Money

Basic Statistics for Putting
your Research Ideas into
Practice

What is statistics?


The collecting, summarizing, and analyzing of
data.



The term also refers to raw numbers, or
“stats”, and to the summarization of data.



Example: Frequencies

Research begins when
there is a question.


Different kinds of
questions:

Descriptive:

Inferential:

How many men work
at Fraser Health?

Does having a science
degree help students
learn statistical
concepts?

How many hours a
week
do employees spend at
their desks?

What risk factors most
predict heart disease?

Types of Statistics


Descriptive Statistics: describe the
relationship between variables.
 E.g. Frequencies, means, standard
deviation



Inferential Statistics: make inferences about
the population, based on a random sample.

Types of Variables


Variables can be classified as independent or
dependent.



An independent variable is the variable that you
believe will influence your outcome measure.



A dependent variable is the variable that is
dependent on or influenced by the independent
variable(s). A dependent variable may also be
the variable you are trying to predict.

Example
A researcher wants to study the effect of Vitamin C on
cancer.
Vitamin C would be the independent variable because
it is hypothesized that it will have an effect on cancer,
and cancer would be the dependent variable because
it is the variable that may be influenced by Vitamin C .
Independent Variable  Direction of Effect  Dependent Variable

Vitamin C

 Increase or
 Cancer
decrease of certain
effect

Variables
 In

research, the characteristic or
phenomenon that can be measured or
classified is called a variable. There are
3 levels of variables:




Nominal
Ordinal
Continuous/Scale

Levels of Data








Nominal= categorical
E.g. Apples and pears, gender, eye colour,
ethnicity.
Data that is classified into categories and
cannot be arranged in any particular order.
 Nominal=Categorical=Dichotomous
Ordinal= data ordered, but distance between
intervals not always equal. E.g. Low, middle
and high income, or rating a brand of soft drink
on a scale of 1-5.
Continuous/Scale= equal distance between
each interval. Also called interval.

Statistical Test Selection
Selecting the appropriate statistical test requires several
steps.


Test selection should be based on:
1)

2)

3)

4)

What is your goal: Description? Comparison? Prediction?
Quantify association? Prove effectiveness? Prove
causality?
What kind of data have you collected? What are the
levels of data (Nominal, ordinal, or continuous)? Was
your sample randomly selected?
Is your data normally distributed? Should you use a
parametric or non-parametric test?
What are the assumptions of the statistical test you would
like to use? Does the data meet these assumptions?

Inferential Statistics


Inferential statistics generally require that data come
from a random sample.



In a random sample each person/object/item of the
population has an equal chance of being chosen.



Various methods of randomization
Excel

Parametric Tests


Parametric tests assume that the variable in question is
from a normal distribution.



Non-parametric tests do not require the assumption of
normality.



Most non-parametric tests do not require an interval level
of measurement; can be used with nominal/ordinal level
data.

Parametric vs. NonParametric
Some Advanced Statistics...

Statistics

Descriptive

Inferential
Parametric

Non-Parametric

T-test

Mann-Whitney U test

ANOVA

Kruskal-Wallis test

Pearson’s R

Spearman’s R

Some examples of parametric and non-parametric tests.

Non-Parametric Tests


Use when all
assumptions of
parametric statistics
cannot be met.



Can be used with data
that are not normally
distributed.

Assumptions







There are various assumptions for each test.
Before you select a test, be sure to check the assumptions of
each test.
You will need to contact a consultant, or review
statistical/research methods resources to find this information.
Some examples of common assumptions are:
 The dependent variable will need to be measured on a
certain level (i.e. Interval level).
 The independent variable(s) will need to be measured on a
certain level (i.e. Ordinal level).
 The population is normally distributed (not skewed).

If your data do not meet the assumptions for a specific test,
you may be able to use a non-parametric test instead.

Sample Size


There are several rules of thumb for determining sample size.





1) It’s a good idea to have a minimum of 30 cases (as a total group, or
if comparing groups, 30 for each group).
If you have less you can use a non-parametric test, but it is still better
to have close to 30 cases.
2) If using regression, it is best to have between 10-50 cases per
independent variable.
3) If you are validating a survey, it is never good to have more
questions than cases.
4) If the total population that you are examining is less than 30. Use all
of them.
5) For pilot studies the recommendation is a sample size of 12 per
group
6) With a sample size of 400 per group, you can do just about anything.



7) For surveys, a 30% response rate is the bare minimum.



Note: For a precise sample size estimate you will need to conduct a power
analysis.








Type of Data
Goal

Measurement
Normal
Population

Ordinal, or
NonNormal
Population
Median,
interquartil
e range
Wilcoxon test

Binomial
Survival Time
-Two
Possible
Outcomes
Proportion
Kaplan Meier
survival curve

Describe one
group

Mean, SD

Compare one
group to a
hypothetic
al value

One-sample t
test

Compare two
unpaired
groups

Unpaired t test

Mann-Whitney
test

Log-rank test or
Mantel-Haenszel*

Paired t test

Wilcoxon test

Fisher's test
(chisquare
for large
samples)
McNemar's
test

Compare two
paired
groups

Chi-square
test

Cox proportional
hazard
regression**

One-way
Compare
ANOVA
three or
more
unmatched
groups

Kruskal-Wallis
test

Chi-square
or
Binomial
test **

Conditional
proportional
hazards
regression*

Type of Data

Goal

Measurement
Normal
Population

Rank, Score, or
Measurement
Non-Normal
Population
Friedman test

Binomial
-Two
Possible
Outcomes
Cochrane Q**

Compare three
or more
matched
groups

Repeatedmeasures
ANOVA

Quantify
association
between two
variables

Pearson
correlation

Spearman
correlation

Contingency
coefficients*
*

Predict value
from
another
measured
variable

Simple linear
regression
or
Nonlinear
regression

Nonparametric
regression**

Simple logistic
regression*

Predict value
from several
measured or
binomial
variables

Multiple linear
regression*
or
Multiple
nonlinear
regression*
*

Survival Time

Conditional
proportional
hazards
regression**

Cox
proportional
hazard
regression*

Multiple logistic Cox
regression*
proportional
hazard
regression*

During the group exercise…


Steps to choose the appropriate statistical method
for the data analysis:

1. Identify whether the research problem raises the
question of describe, relate (association), or compare
(difference).
2. Identify the levels of measurement in the research
question (Nominal/Categorical, Ordinal/Rank,
Continuous/Evenly spaced).
3. Identify the number of variables, or samples being
described, related, or compared.
4. Identify whether comparison samples are related
(analyze same group before and after) or independent
(not at all related, looking at different groups).
5. Choose the appropriate statistical tool for the data and
situation using the decision tree in the handout.

WORK GROUP ASSIGNMENT
Now it’s time to meet
your research team!
 You will be assigned
to groups.
 Get ready to meet
your Co-investigators!




1. A pilot experiment designed to test the effectiveness of a new
approach to electrode placement for Electro Shock Therapy
(ECT) has been conducted over a one year time period in the
Fraser Health Authority.
Patients from two different mood disorder clinics participated in
this study. Patients from Clinic X received ECT therapy
according to current practice guidelines. Patients from Clinic Y
received a new exploratory ECT treatment. Patients in each
clinic were matched for age, gender, and type of disorder. A
random sample of 30 matched pairs of patients were selected
for inclusion in the study. At end of one year, patients were
administered a memory test yielding a total score out of 100.
Dr. Vasdil would like to know what statistical procedure needs
to be selected to test for differences among groups of patients
on the memory test.
What is the question: Compare
How many samples: 2
Related or independent: Independent
What is the level of measurement: Continuous
How many dependent variables: 1
Test: T-test

Presentation of Group Exercise

BONUS: Can you really sing in
statistics? Prove it!


What category of statistical test could you use
if you have a sample of less than 30 people?



What is the recommended sample size for
pilot studies?



Quantitative research is deductive, qualitative
research is ________.



Reliability, validity, and objectivity are all
important ________ considerations.

Research is fun…who knew?

Please see the FH Research Administration and
Development research education calendar for more
upcoming events.
http://www.fraserhealth.ca/NR/rdonlyres/exxf5kfjcwnk5sguhbxsb7hib54u
zimhfjx5z2pw7ckfjngkqsvi2txpxsrb7hlrdks7pkys6cnu7i/FHResearchEducat
ionSchedule0708.pdf

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