Transcript Unit 4 Data Collection and Measurement.ppt

Data Collection, Measurement, &
Data Quality in Quantitative and
Qualitative Research
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Data Collection Methods
• Without appropriate data collection methods, the
validity of research conclusions is easily
challenged
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Data Collection Methods
• Using New Data
– Collect own data for the
study
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Data Collection Methods
• Using Existing Data
– Historical research
• Use records and other
documents from the past
– Secondary analysis
• Use of data gathered in a
previous study
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Key Dimensions of
Data Collection Methods
• Structure
– The data collection should be very structured and consistent
• Quantifiability
– Able to be analyzed statistically
• Obtrusiveness
– Degree to which people are aware that they are being studied
• Objectivity
– Try to be as objective as possible
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Data Collection
Quantitative Research
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Types of Data Collection
• Self-Reports
• Observation
• Biophysiologic Measures
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Types of Data Collection
• Self-Reports
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Interviews
Questionnaires
Scales
Vignettes
Projective techniques
Q-sorts
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Types of Data Collection: SelfReports
Interviews and Questionnaires (Structured)
• Participant's responses to questions by researcher
• Data is usually collected by means of a formal, written
document (instrument)
• Uses an interview schedule for questions that are asked
orally (face to face or via phone)
• Uses a questionnaire when participants complete the
instrument themselves
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Types of Data Collection: SelfReports
Interviews and Questionnaires (Structured)
– Closed-ended questions (fixed alternative questions)
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Response alternatives are specified by the researcher
Ensures comparability of responses
Facilitates analysis
Easy to administer
More efficient time use
Difficult to develop
Could lead to overlooking something important
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Types of Data Collection: SelfReports
Interviews and Questionnaires (Structured)
– Open-ended questions
• Allows participants to respond to questions in their own
words
• Allows for richer, fuller information
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Types of Data Collection: SelfReports
Interviews and Questionnaires
(Structured)
– Instrument Construction
• Develop outline of content of
research
• Design questions
• Pretest
– Trial run to determine if
instrument is free of biases,
errors, etc
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Types of Data Collection: SelfReports
• Interviews Vs. Questionnaires
– Advantages of questionnaires
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Less costly
Require less time and effort to administer
Can be completely anonymous
No biases relating to the researcher being present
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Types of Data Collection: SelfReports
• Interviews Vs. Questionnaires
– Advantages of Interviews
• Response rate is higher in face to face interviews
• Effective for those that can not complete questionnaires
(children, blind, ESL, elderly)
• Questions are less likely to be misinterpreted than
questionnaires
• Interviews can produce additional information through
observation
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Types of Data Collection: SelfReports
• Interviews Vs. Questionnaires
– Interviews are considered to be superior to questionnaires
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Types of Data Collection: SelfReports
Types of Self-Reports (Structured)
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Composite Scales (social - psychological)
Vignettes
Projective techniques
Q sorts
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Types of Data Collection: SelfReports
Composite Scales (social - psychological)
– Scale: assigns a numeric score to people to place them on a
continuum with respect to attributes being measured
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Types of Data Collection: SelfReports
Composite Scales (social - psychological)
– Likert scale
– Semantic Differential scale
– Visual Analog scale
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Types of Data Collection: SelfReports
Composite Scales (social - psychological)
– Likert scale (summated rating scales)
• Consists of several declarative statements that express a
viewpoint
• Participant indicates the degree to which they agree to
disagree
• Able to summate the scores allowing for discrimination
among people with different viewpoints
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Types of Data Collection: SelfReports
Composite Scales (social - psychological)
Example Likert Scale:
AU nursing students are very well prepared for working within the
current healthcare system
Strongly agree
Agree
Neutral
Disagree
Strongly disagree
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Types of Data Collection: SelfReports
Composite Scales (social - psychological)
– Semantic Differential
• Participants rate a concept on a series of bipolar adjectives
• Can measure any concept
– Visual Analog Scale
• The scale is a straight line with anchors which are the
extreme limits of the experience or feeling
• Measures subjective experiences
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Types of Data Collection: SelfReports
– Semantic Differential
Example
AU nursing graduates are:
Competent
Incompetent
Intelligent
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– Visual Analog Scale
Example
On a scale of 0 to 10 how would you rate your pain if 10 was
the worst pain you have even experienced and 0 was no pain
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Advantages of Scales
– Scales allow researchers to efficiently quantify the
strength and intensities of individual characteristics
– Discriminates among people with different attitudes,
fears, motives, perceptions, personality traits, needs
– Good for group and individual comparisons
– Can be implemented either verbally or in writing
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Disadvantages of Scales
Response set biases
– Social Desirability Response Set Bias
• Participants give answers that are common social views
– Extreme Response Set Bias
• Participants express attitudes or feelings in the extreme (always, never)
– Acquiescence Response Set Bias
• Participants agree with all statements (yea-sayers or nay-sayers)
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Disadvantages of Scales
– Ways to Reduce Response Set Biases
• Counterbalancing: positively and negatively worded
statements
• Developing sensitively worded questions
• Creating a permissive, nonjudgmental atmosphere
• Guaranteeing confidentiality
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Types of Data Collection: SelfReports
Vignettes
– Brief description of events or situations to which
participants are asked to react
– Information about perceptions, opinions, or knowledge
– Questions post vignettes may be open-ended or closeended
– Economical to administer
– May contain response biases
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Types of Data Collection: SelfReports
Projective Techniques
– Verbal self reports to obtain psychological
measurements
– Seek minimal participants’ conscious cooperation
– Ambiguous or unstructured stimuli elicits participants
needs, motives, attitudes, personality traits
i.e. Inkblot test, word association, role playing, drawing
– Useful in children, hearing or speech impaired
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Types of Data Collection: SelfReports
Q Sorts
– Uses a set of card with words, phrases or statements
– Participant sorts cards along a bipolar dimension
(agree/disagree)
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Advantages of Self-Reporting
Methods
• Most common method of data collection used by
nurses
• Reveal information that is difficult to obtain by
other means
• Can gather retrospective and prospective data
• Can measure psychological characteristics
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Disadvantages of Self-Reporting
Methods
• Questionable validity and
accuracy
• Biases
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Types of Data Collection:
Observation
• Observational Methods
– An alternative to self-reports
– Can be used to gather information such as characteristics,
condition of individuals, verbal communication, nonverbal
communication, activities, environmental conditions
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Types of Data Collection:
Observation
Observational Methods
• Researcher has flexibility in the following areas:
– The focus of observation
• What events are to be observed
– Concealment
– Duration of observation
– Method of recording observations
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Types of Data Collection:
Observation
Observational Methods (structured)
– Categories and checklists
– Rating Scales
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Types of Data Collection:
Observation
Categories and Checklists
• Category system:
– attempts to designate information in a systematic, quantitative
manner
– Clear definition of behaviors and characteristics to be observed is
necessary
– Lists all behaviors or activities the observer wants to observe and
records occurrences
• Checklist:
– instrument to record observations
• Rating Scales:
– Are tools that require the observer to rate some phenomena along34a
descriptive continuum
Types of Data Collection:
Observation
– Observational Sampling
• Time sampling
– Selection of time periods for observations
• Event sampling
– Selects behaviors or events for observation
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Evaluation of Observational
Methods
• Advantages
– Provides depth and variety of information
– Some problems are better suited to observation
• Disadvantages
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Potential ethical issues
Lack of consent to be observed
Participants reaction to be observed
Biases
• Faulty inferences
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Types of Data Collection
Biophysiologic
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Types of Data Collection:
Biophysiologic
Types of Biophysiologic Measures
– In vivo
• Measures performed directly within or on living organisms
– i.e. blood pressure, temperature
– In vitro
• Data gathered from participants by extracting some
biophysiologic material from them for lab analysis
– i.e. blood work, microbiologic measures, cytology and histological
measures
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Advantages of Biophysiologic
Measures
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Are relatively accurate and precise
Are objective
Provide valid measures of targeted variables
Equipment is readily available
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Disadvantages of Biophysiologic
Measures
– Measuring tool may affect variables it is attempting to
measure
– Interferences may create artifact
– Energy must often be applied to the organism when
taking measurements
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• Measurement and Assessment of Data
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Measurement
• Involves rules for assigning numeric values to
qualities
• Determines how much of an attribute is present
• Quantification
– Communicates the amount in numbers
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Advantages of Measurement
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Removes guesswork in gathering information
Tends to be objective
Obtains precise information
Can differentiate among people who possess different
degrees of an attribute
– Common language
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Errors of Measurement
– Always the potential for error in all tools
– Extraneous factors affect measurement and distort
results
• Obtained score – is observed score
• True score – true score if no errors
• Error of measurement – the different between the true and
obtained scores
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Factors Contributing to
Errors of Measurement
– Situational contaminants
» People’s awareness of observer, environmental
factors
– Response set biases
– Transitory personal factors
» Fatigue, mood, hunger (temporary)
– Administration variations
» Alterations in data collection methods
– Item sampling
» Errors introduced as a result of sampling
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Reliability of Measuring Instruments
Reliability
– Refers to the consistency with which an instrument
measures the attribute
– The less variation in repeat measures the higher its reliability
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Reliability of Measuring Instruments
Reliability
– Aspects of reliability
• Stability
• Internal consistency
• Equivalence
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Reliability of Measuring Instruments
• Stability
• The extent to which the same scores are obtained when
the instrument is used with the same people on separate
occasions
• To assess stability: Test-retest reliability
– researcher administers the same measure to a sample of
people on two occasions and then compares the scores
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Reliability of Measuring Instruments
• Internal Consistency
• Reliable to the extent that all its subparts measure the
same characteristic
• To assess internal consistency: Split-half technique
– the items comprising the test or scale are split into two
groups and scored, compute reliability coefficient
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Reliability of Measuring Instruments
• Equivalence
• Determines the consistency or equivalence of the
instrument by different observers or raters
• To assess equivalence – interrater (interobserver) reliability
– Has two or more trained observers make simultaneous,
independent observations, compete reliability coefficient
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Reliability of Measuring Instruments
• Reliability Coefficients
– A quantitative statistic that estimates how reliable an
instrument is
• Determine an instrument’s quality
• Low reliability makes it difficult to adequately test research
hypothesis
• If sample too homogeneous, the lower reliability coefficient
will be (instruments are designed to measure differences)
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Validity of Measuring Instruments
Validity
– Is the concern whether the measurement tools actually
measure what they are supposed to measure
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Validity of Measuring Instruments
Aspects of Validity
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Face validity
Content validity
Criterion-related validity
Construct validity
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Validity of Measuring Instruments
– Face validity
• Whether the instrument
looks as though it is
measuring the
appropriate construct
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Validity of Measuring Instruments
– Content Validity
• Concerned with
adequacy of coverage
of the content area
being measured
– Tests of knowledge
– Psychosocial traits
– Based on judgment
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Validity of Measuring Instruments
– Criterion-Related
Validity
• Wants to establish the
relationship between
scores on an instrument
and some external
criterion
– Compute a validity
coefficient –
correlates scores on
the instrument with
scores in the
criterion variable
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Validity of Measuring Instruments
– Construct Validity
• Concerned with what
construct is the
instrument actually
measuring
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Validity of Measuring Instruments
• To assess construct validity–
– known-groups technique
» Groups that are expected to differ on certain
attributes are administered the instrument then
scores are compared
– Factor analysis
» Statistical procedure
– Examination of relationships based on theoretical
predictions
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Reliability of Measuring Instruments
• If a measuring device is not reliable, it can not be valid
– High reliability of an instrument provides no evidence of its
validity
– Low reliability is evidence of low validity
• An instrument can be reliable without being valid
Reliability consistently measures accurately
Validity measures what it is supposed to
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Data Collection
Qualitative Research
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Questions for Thought
• What are the systematic rules for analyzing
qualitative data?
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Qualitative Data Collection
Types of Self-Reports - Unstructured
Self-Reports Methods (Unstructured)
• Interviews
• Diaries
• Observation
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Qualitative Data Collection
Types of Self-Reports - Unstructured
Interviews
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Flexible
Not directed by set questions
Interviews are conversational in nature
Usually interviews are long
Can be tape recorded or researcher may take notes
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Qualitative Data Collection
Types of Self-Reports - Unstructured
• Completely unstructured interviews
• Start with broad (grand tour) questions
• Further questions are guided by initial responses – one
question's answer leads to the next question
• Focused or semi-structured interviews
• Researcher lists topics that must be covered in an interview
• Uses a topic guide to ensure all question areas are covered
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Qualitative Data Collection
Types of Self-Reports - Unstructured
• Focus group interviews
• Interviews with groups of 5 to 15 people whose opinions and
experiences are solicited simultaneously
• Uses topic guide to guide questions
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Qualitative Data Collection
Types of Self-Reports - Unstructured
• Life Histories
• Narrative self-disclosures about life experiences
• Has informants describe experiences in chronological order
• Orally or written
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Qualitative Data Collection
Types of Self-Reports - Unstructured
Diaries
• Have informants
maintain daily logs of
some aspect of their
lives
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Qualitative Data Collection:
Observational Methods - Unstructured
Observational Methods
• Unstructured observation
– Attempt to see the world as the participants see it
– Participant observation – data collector actually participates in the
group
» Participation can be from the role as an observer or totally
immersed in the social setting as a participant
» Researcher needs to gain entrée into the social group under
investigation
» Researcher needs to establish rapport and develop trust within
the group
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Qualitative Data Collection:
Observational Methods - Unstructured
• Observational Data Collection
– Physical setting
» In what context is the human behaviour occurring
– Participants
» Information about the participants, what are their roles,
characteristics
– Activities
» What are the participants doing
– Frequency and duration
» Specific information about the activity
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Qualitative Data Collection:
Observational Methods - Unstructured
– Process
» How is event occurring
– Outcomes
» Why is the activity occurring and what are the results
– Single positioning
» Staying in a single location
– Multiple positioning
» Involves moving around to observe behaviour from different
perspectives
– Mobile positioning
» Involves following a person throughout a given activity
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Qualitative Data Collection:
Observational Methods - Unstructured
• Observational Data Recording
• Uses logs and field notes
» Log – records daily events
» Field notes – observer’s efforts to record information and
understand data
» Observational notes – descriptions of events and conversations
» Theoretical notes – interpretive attempts to attach meaning to
observations
» Methodologic notes – instructions about what observations that
need to be made
» Personal notes – comments about researcher’s own feelings
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Assessment of Qualitative Data
– Do the measures used by the researcher yield data reflecting
the truth
– Qualitative research attempts to do this through establishing the
data’s trustworthiness
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Assessment of Qualitative Data
Establish Trustworthiness by assessing:
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1. Credibility
2. Dependability
3. Confirmability
4. Transferability
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Assessment of Qualitative Data:
Trustworthiness
1. Credibility
– Confidence in the truth of the data
• Prolonged engagement and persistent observation
– Sufficient time to collect data, focus on the phenomena being studied
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Assessment of Qualitative Data:
Trustworthiness
Triangulation
– Use of multiple referents to draw conclusions, attempts to distinguish true
information from errors
– Data Source Triangulation
» Multiple data sources (interviewing diverse informants on same topic)
– Investigator Triangulation
» Using more than one person to collect data
– Theory Triangulation
» Using multiple perspectives to interpret data
– Method Triangulation
» Using multiple methods (observation and interviews)
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Assessment of Qualitative Data:
Trustworthiness
• External checks: Peer debriefing and member checks
– Peer debriefing – review and explore various aspects of inquiry with
objective peers
– Member checks – providing feedback to study participants and
assessing their reactions
• Searching for Disconfirming evidence
– Search for data that challenges the emerging conceptualization or
theory
• Researcher credibility
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Assessment of Qualitative Data:
Trustworthiness
2. Dependability
– Data stability over time and over conditions
– Stepwise replication
• Having several researchers break into teams and evaluate
the data separately and then compare conclusions
– Inquiry audit
• Scrutiny of the data and supporting documents by an
external reviewer
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Assessment of Qualitative Data:
Trustworthiness
3. Confirmability
– The objectivity or neutrality of the data, can other
independent people agree about data’s relevance
– Audit trail – documentation that allows an independent
auditor to come to the same conclusions about the
data
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Assessment of Qualitative Data:
Trustworthiness
4. Transferability
– The extent to which the findings from the data can be
transferred to other settings or groups
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Reference
• Loiselle, C. G., Profetto-McGrath, J., Polit, D. F., &
Beck, C. T. (2011). Canadian essentials of nursing
research. (Third Edition). Philadelphia: Lippincott,
Williams & Wilkins.
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