Transcript Chapter 8 Population & Sample

Chapter 9
Examining Populations and Samples
in Research
Copyright © 2011 by Saunders, an imprint of Elsevier Inc.
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Sampling Concepts
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Sampling: Selecting a group of people,
events, behaviors, or other elements with
which to conduct a study
Sampling plan: Sampling method; defines
the selection process
Sample: Defines the selected group of
people or elements from which data are
collected for a study
Members of the sample can be called the
subjects or participants.
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Populations and Elements
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Population: A particular group of individuals or
elements who are the focus of the research
Target population: An entire set of individuals or
elements who meet the sampling criteria
Accessible population: The portion of the target
population to which the researcher has reasonable
access
Elements: Individual units of the population and
sample
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Generalization
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Extending the findings from the sample under
study to the larger population
The extent is influenced by the quality of the
study and consistency of the study’s findings.
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Sampling Criteria: Inclusion
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Characteristics that the subject or element
must possess to be part of the target
population
Examples:
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Between the ages of 18 and 45
Ability to speak English
Admitted for gallbladder surgery
Diagnosed with diabetes within past month
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Sampling Criteria: Exclusion
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Characteristics that can cause a person or
element to be excluded from the target
population
Examples:
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Diagnosis of mental illness
Less than 18 years of age
Diagnosis of cognitive dysfunction
Unable to read or speak English
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Defining Sampling Criteria
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Homogeneous sample: As similar as
possible so as to control for extraneous
variables
Heterogeneous sample: Represents a
broad range of values
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Used when a narrow focus is not desirable
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Inappropriate Generalizations
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Samples cannot be generalized beyond their
sampling criteria.
This may lead to inappropriate
generalizations:
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Because of language or reading ability
To other types of illnesses or injuries
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Representativeness
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The sample, the accessible population, and
the target population are alike in as many
ways as possible.
Need to evaluate:
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Setting
Characteristics of subjects (age, gender, ethnicity,
income, education)
Distribution of values on variables measured in the
study
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Sampling Error
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Difference between the population mean and
the mean of the sample
Random variation
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The expected difference in values that occurs
when different subjects from the same sample are
examined
Difference is random because some values will be
higher and others lower than the average
population values
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Sampling Error (cont’d)
Sampling error
Sample
Population
Population
mean
Sample
mean
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Sampling Error (cont’d)
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Systematic variation (bias)
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Consequence of selecting subjects whose
measurement values differ in some specific way
from those of the population
These values do not vary randomly around the
population mean.
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Random vs. Systematic Variation in
Sampling
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Random variation: Expected difference in
values that occurs when different subjects
from same sample are examined
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Difference is random because some values will be
higher or lower than the mean population value.
As sample size increases, random variation
decreases.
Systematic variation (or systematic bias):
Consequence of selecting subjects whose
measurement values differ in some way from
those of the population
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Refusal Rate vs. Acceptance Rate
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Refusal rate: Percentage of subjects who
declined to participate in the study
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80 subjects approached and 4 refused
4  80 = 0.05 = 5% refusal rate
Acceptance rate: Percentage of subjects
who consented to be in the study
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80 subjects approached and 76 accepted
76  80 = 0.95 = 95% acceptance rate
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Sample Attrition and Retention
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Sample attrition: Withdrawal or loss of
subjects from a study
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Attrition rate = number of subjects withdrawing ÷
number of study subjects × 100
Sample retention: Number of subjects who
remain in and complete a study.
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Random Sampling
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Increases the representativeness of the
sample based on the target population
Control group: Used in studies with random
sampling
Comparison group: Not randomly determined
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Sampling Frame and Sampling Plan
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Sampling frame: A listing of every member
of the population, using the sampling criteria
to define membership in the population
Subjects are selected from the sampling
frame
Sampling plan: Outlines strategies used to
obtain a sample for a study
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Probability sampling plans
Nonprobability sampling plans
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Types of Probability Sampling
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Simple random sampling
Stratified random sampling
Cluster sampling
Systematic sampling
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Simple Random Sampling
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Randomly choosing the sample
Can use a table of random numbers
Can draw names out of a hat
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Stratified Random Sampling
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Ensures all levels of identified variables are
adequately represented in the sample
Needs a large population with which to start
Variables often stratified
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Age, gender, socioeconomic status
Types of nurses, sites of care
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Cluster Sampling
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All areas with the elements of the identified
population are linked.
A randomized sample of these areas is then
chosen.
Used to get a geographically diverse sample
Also used when developing a sampling frame
is difficult because of a lack of knowledge of
the variables
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Systematic Sampling
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Selecting every kth individual on the list,
starting randomly
Researcher must know number of elements
in the population and the sample size desired
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Nonprobability Sampling
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Quantitative research
 Convenience
(accidental) sampling
 Quota sampling
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Convenience Sampling
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Also called accidental sampling
Weak approach to sampling because it is
hard to control for bias
The sample includes whomever is available
and willing to give consent.
Representativeness is a concern.
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Quota Sampling
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Uses convenience sampling, but with a
strategy to ensure inclusion of subject types
who are likely to be underrepresented in the
convenience sample
Goal is to replicate the proportions of
subgroups present in the population
Works better than convenience sampling to
reduce bias
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Sample Size in Quantitative Studies
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Affect size
Type of quantitative study conducted
Number of variables
Measurement sensitivity
Data analysis techniques
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Power Analysis
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Ability to detect differences in the population
or capacity to correctly reject a null
hypothesis
Standard power of 0.8
Level of significance
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Alpha = 0.05, 0.01, 0.001
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Effect Size
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The effect is the presence of the
phenomenon being studied.
The effect size is the extent to which the null
hypothesis is false.
When the effect size is large (large variation
between groups), only a small sample is
needed.
Increasing the sample size increases the
effect size.
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Number of Variables
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As the number of variables increases, the
sample size may increase.
The inclusion of multiple dependent variables
also increases the sample size needed.
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Measurement Sensitivity
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Was the tool used a reliable and valid
measure of the variable?
As the variance in the instrument scores
increases, the sample size needed to obtain
significance increases.
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Data Analysis Techniques
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ANOVA and t-test require equal group sizes,
which will increase power because the effect
size is maximized.
Chi-square is the weakest of the tests and
requires a large sample size to achieve
acceptable levels of power.
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As the number of categories increases, the
sample size must increase as well.
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Critiquing the Sample
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Identify
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Elements
Accessible population
Target population
Evaluate
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Appropriateness of generalization in quantitative
studies
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Critiquing the Sample (cont’d)
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Identify the sample criteria.
Judge appropriateness of the sampling
criteria.
Identify the sampling method.
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Nonprobability Sampling
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Qualitative research
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Purposive sampling
Network or snowball sampling
Theoretical sampling
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Purposeful or Purposive Sampling
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Also called judgmental or selective sampling
Efforts are made to include typical or atypical
subjects.
Sampling is based on the researcher’s
judgment.
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Network Sampling
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Also called snowball sampling
Takes advantage of social networks to get the
sample
One person in the sample asks another to
join the sample, and so on.
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Theoretical Sampling
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Used in grounded theory research
Data are gathered from any individual or
group that can provide relevant data for
theory generation.
The sample is saturated when the data
collection is complete based on the
researchers’ expectations.
Diversity in the sample is encouraged.
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Sample Size in Qualitative Research
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Scope of the study
Nature of the topic
Quality of the data
Study design
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Scope of the Study
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Broad studies require larger samples than
narrow studies.
The sample size must be adequate for the
scope.
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Nature of the Topic
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If the study topic is clear, fewer subjects are
needed.
If the topic is difficult to define, then a larger
sample is needed.
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Quality of the Data
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How rich are the data?
Were data collected from the best sources?
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Study Design
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How many interviews were carried out?
Was the design adequate for the variables?
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Adequacy of the Sample in Qualitative
Studies
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Are the sampling inclusion and exclusion
criteria appropriate?
Is the sampling plan adequate to address the
purpose of the study?
Is the sample size adequate?
What are the refusal and mortality rates?
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Adequacy of the Sample in Qualitative
Studies (cont’d)
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Are sample characteristics and quality
described?
Is there saturation of the data?
Is the setting defined?
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Research Settings
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Natural or field setting: uncontrolled in real life
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Partially controlled setting: manipulated or
modified by the researcher
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Seen in descriptive or correlational studies
Seen in correlational, quasi-, or experimental
studies
Highly controlled setting: artificially
constructed by researcher (i.e., lab setting)
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Seen in experimental studies
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