psychometric-instrument-development-reliabilities-and-composite-scores-1206978537846832-2.odp

Survey Design II

Lecture 6
Survey Research & Design in Psychology
James Neill, 2013

Psychometric Instrument Development

Image source: http://commons.wikimedia.org/wiki/File:Soft_ruler.jpg, CC-by-SA 3.0

7126/6667 Survey Research & Design in PsychologySemester 1, 2013, University of Canberra, ACT, AustraliaJames T. NeillHome page: http://en.wikiversity.org/wiki/Survey_research_and_design_in_psychologyLecture page: http://en.wikiversity.org/wiki/Psychometric_instrument_development

Image source:http://commons.wikimedia.org/wiki/File:Soft_ruler.jpgImage author: Lite, http://commons.wikimedia.org/wiki/User:LiteImage license: GFDL 1.2+, CC-by-SA 3.0 unported

Description: This lecture recaps the previous lecture on exploratory factor analysis, and introduces psychometrics and (fuzzy) concepts and their measurement, including (operationalisation), reliability (particularly internal consistency of multi-item measures), validity and the creation of composite scores.

This lecture is accompanied by a computer-lab based tutorial.http://en.wikiversity.org/wiki/Survey_research_and_design_in_psychology/Tutorials/Psychometrics

Overview

Recap: Exploratory factor analysis

Concepts & their measurement

Measurement error

Psychometrics

Reliability & validity

Composite scores

Writing up instrument development

Image source: http://commons.wikimedia.org/wiki/File:Information_icon4.svgLicense: Public domain

Bryman & Cramer (1997). Concepts and their measurement. [Chapter - eReserve]

DeCoster, J. (2000). Scale construction notes. http://www.stat-help.com/scale.pdf

Howitt & Cramer (2005). Reliability and validity: Evaluating the value of tests and measures. [Chapter eReserve]

Howitt & Cramer (2011). Reliability in scales and measurement: Consistency and measurement. [Chapter Textbook]

Wikiversity. Measurement error - http://en.wikiversity.org/wiki/Measurement_error

Wikiversity. Reliability and validity - http://en.wikiversity.org/wiki/Reliability_and_validity

Readings: Psychometrics

Recap:
Exploratory Factor Analysis


What is factor analysis?

Factor analysis is: a family of multivariate correlational data analysis methods

used to identify clusters of covariance (called factors)

Two main types (extraction methods):Exploratory factor analysis (EFA)

Confirmatory factor analysis (CFA)

EFA assumptions

Sample size5+ cases per variables (min.)

20+ cases per variable (ideal)

Another guideline: Or N > 200

Check bivariate outliers & linearity

Factorability: check any of:Correlation matrix: Some over .3?

Anti-image correlation matrix diags > .5

Measures of Sampling AdequacyKMO > ~ .5 to 6; Bartlett's sig?

Normality enhances the solution

Summary of EFA steps / process

Test assumptions

Sample size, Outliers & linearity, Factorability

Select type of analysis

Extraction (PC/PAF),

Rotation (Orthorgonal(Varimax)/Oblique(Oblimin)

Image source: http://commons.wikimedia.org/wiki/File:Information_icon4.svgLicense: Public domainSee also http://en.wikiversity.org/wiki/Exploratory_factor_analysis/Data_analysis_tutorialSee also http://en.wikiversity.org/wiki/Exploratory_factor_analysis/Data_analysis_tutorial


Determine no. of factors

Theory

Kaiser's criterion

Eigen Values and Scree plot

% variance explained

Interpretability of weakest factor

Select items

Check factor loadings to identify which items belong in which factor

Drop items 1-by-1 if primary loading low

cross-loadings high

item wording doesn't belong to meaning of factor



Name and define factors

Examine correlations amongst factors

Check factor structure for sub-groups

Analyse internal reliability

Compute composite scores

Covered in this lecture


271 UC students responded to 24 university student motivation statements in 2008 using an 8-point Likert scale (False to True) e.g.,
I study at university

to enhance my job prospects.

because other people have told me I should.

EFA PC Oblimin revealed 5 factors

Example EFA:
University student motivation


Example EFA:
Pattern matrix


Example EFA:
University student motivation

Career & Qualifications (6 items; = .92)

Self Development (5 items; = .81)

Social Opportunities (3 items; = .90)

Altruism (5 items; = .90)

Social Pressure (5 items; = .94)


Example EFA:
Factor correlations

MotivationCQSDSOALSP

Career & Qualif..26.25.24.06

Self Develop..33.55-.18

Social Enjoyment.26.33

Altruism.11

Social Pressure


Example EFA:
Error-bar graph


Exploratory factor analysis: Q & A

Questions?

Concepts &
their measurement

Operationalising
fuzzy concepts


Concepts & their measurement:
Bryman & Cramer (1997)

Conceptsform a linchpin in the process of social research

express common elements in the world (to which we give a name)

Hypotheses express relations between concepts

Bryman & Cramer Concepts & their Measurement (1997):
"Concepts form a linchpin in the process of social research. Hypotheses contain concepts which are the products of our reflections on the world. Concepts express common elements in the world to which we give a name." (p. 53)


Once formulated, a concept will need to be operationally defined, in order for systematic research to be conducted in relation to it..."

Bryman & Cramer Concepts & their Measurement (1997):Once formulated, a concept and the concepts with which it is purportedly associated, such as social class and authoritarianism, will need to be operationally defined, in order for systematic research to be conducted in relation to it..."


...An operational definition specifies the procedures (operations) that will permit differences between individuals in respect of the concept(s) concerned to be precisely specified..."

Bryman & Cramer Concepts & their Measurement (1997)


...What we are in reality talking about here is measurement, that is, the assignment of numbers to the units of analysis - be they people, organizations, or nations - to which a concept refers."

Bryman & Cramer Concepts & their Measurement (1997)

Image (left) source:http://www.flickr.com/photos/iliahi/408971482/By Maui in Vermont: http://www.flickr.com/photos/iliahiLicense: CC-by-A 2.0

Image (right) source: http://commons.wikimedia.org/wiki/File:3DTomo.jpgBy Tomograph: http://de.wikipedia.org/wiki/Benutzer:TomographLicense: GFDL

Operationalisation

...is the act of making a fuzzy concept measurable.

Social sciences often use multi-item measures to assess related but distinct aspects of a fuzzy concept.

Image source: http://commons.wikimedia.org/wiki/Image:Die_bone.jpgCreative Commons Attribution 3.0 Unported

Operationalisation steps

Brainstorm indicators of a concept

Define the concept

Draft measurement items

Pre-test and pilot test

Examine psychometric properties
how precise are the measures?

Redraft/refine and re-test

This is a cyclical or iterative process.

Operationalisating a fuzzy concept: Example (Brainstorming indicators)

Image source: James Neill, Creative Commons Attribution-Share Alike 2.5 Australia, http://creativecommons.org/licenses/by-sa/2.5/au/

Fuzzy concepts - Mindmap

Nurse empowerment

Image source: James Neill, Creative Commons Attribution 3.0; Adapted from original image source: http://www.uwo.ca/fhs/nursing/laschinger/image002.gif (Retreived 20 March, 2007). As of 25 March, 2008, the original image could no longer be found. It was based on research by Laschinger and colleagues on nurse empowerment.

Factor analysis process

Image source: Figure 4.2 Bryman & Cramer (1997)

Image source: Figure 4.2 Bryman & Cramer (1997)

Measurement error

Image source: http://commons.wikimedia.org/wiki/File:Noise_effect.svg

Image source: http://commons.wikimedia.org/wiki/File:Noise_effect.svgImage license: GFDL 1.2+

Measurement precision & noise

The lower the precision, the more subjects you'll need in your study to make up for the "noise" in your measurements. Even with a larger sample, noisy data can be hard to interpret. And if you are an applied scientist in the business of testing and assessing clients, you need special care when interpreting results of noisy tests.

http://www.sportsci.org/resource/stats/precision.html

Measurement error

Measurement error is any deviation from the true value caused by the measurement procedure.Observed score =

true score + measurement errorMeasurement error =

systematic error + random error

Sources of measurement error

Non-sampling
(e.g., unreliable
or invalid
tests)Sampling
(e.g., non-rep. sample)Researcher bias
(e.g., researcher favours a hypothesis)Paradigm
(e.g., Western focus on individualism)

Respondent bias
(e.g., social desirability)

Image source:s Unknown.Paradigm (e.g., assumptions, focus, collection method)

Personal researcher bias (conscious & unconscious)

Sampling (e.g., representativeness of sample)

Non-sampling (e.g, non-response, inaccurate response due to unreliable measurements, misunderstanding, social desirability, faking bad, researcher expectancy, administrative error)

To minimise measurement error

Use well designed measures:Multiple indicators for fuzzy constructs

Sensitive to target constructs

Clear instructions and questions

Reduce demand effects:Train interviewers

Use standard administration survey protocol



Obtain a representative sample:Use probability-sampling if possible

Minimise bias in selection for non-probability sampling

Maximise response rate:Pre-survey contact

Minimise length / time / hassle

Offer rewards / incentives

Coloured paper

Call backs / reminders

Ensure administrative accuracy:Set up efficient coding, with well-labelled variables

Check data (double-check at least a portion of the data)


Psychometrics


Image source: http://www.flickr.com/photos/ajawin/2614485186/By akawin http://www.flickr.com/photos/ajawin/License: Creative Commons by Attribution 2.0 http://creativecommons.org/licenses/by/2.0/deed.en

Psychometrics: Goal

To validly measure differences between individuals and groups in psychosocial qualities such as attitudes and personality.

Psychometrics is the field of study concerned with the theory and technique of educational and psychological measurement, which includes the measurement of knowledge, abilities, attitudes, and personality traits. The field is primarily concerned with the study of differences between individuals and between groups of individuals. http://en.wikipedia.org/wiki/Psychometrics

Psychometrics: As test-taking grows, test-makers grow rarer

"Psychometrics, one of the most obscure, esoteric and cerebral professions in America, is now also one of the hottest.
- As test-taking grows, test-makers grow rarer, David M. Herszenhor, May 5, 2006, New York Times

e.g., due to increased testing of educational and psychological capacity and performance

Image source: http://commons.wikimedia.org/wiki/Image:Framework_complexity_of_the_Pater_Noster_lighthouse.jpgGNU Free Documentation license, Version 1.2 or any laterhttp://www.nytimes.com/2006/05/05/education/05testers.htmlIn Australia, probably the largest collection of psychometricians is at the Australian Council for Educational Research:http://www.acer.edu.au/

Psychometric tasks

Develop approaches and procedures (theory and practice) for measurement of psychological phenomena

Design and test psychological measurement instrumentation
e.g., examine and improve reliability and validity

Psychometrics is the field of study concerned with the theory and technique of educational and psychological measurement, which includes the measurement of knowledge, abilities, attitudes, and personality traits. The field is primarily concerned with the study of differences between individuals and between groups of individuals. http://en.wikipedia.org/wiki/Psychometrics

Not everything that counts...

But remember

Image source: Unknown

Psychometric methods

Factor analysisExploratory

Confirmatory

Classical test theoryReliability

Validity

Item response modeling

Reliability & Validity

Image source: http://www.flickr.com/photos/psd/17433783/in/photostream, CC-by-A 2.0


Image source: http://www.flickr.com/photos/psd/17433783/in/photostreamBy psd http://www.flickr.com/photos/psd/License: Creative Commons by Attribution 2.0 http://creativecommons.org/licenses/by/2.0/deed.en

Reliability and validity
(Howitt & Cramer, 2005)

Reliability and validity are the means by which we evaluate the value of psychological tests and measures.Reliability is about the consistency of the items within the measure

the consistency of a measure over time

Validity concerns the evidence that the measure actually measures what it is intended to measure.

Internal consistency (single administration) and Test-reliability (multiple administrations) are based on classical test theory. A more advanced/refined reliability technique is based Item Response Theory, which involves examining the extent to which each item discriminates between individuals with high/low overall scores.For more info see: http://en.wikipedia.org/wiki/Reliability_(psychometric)

Reliability vs. validity

In classical test theory, reliability is generally thought to be necessary for validity, but it does not guarantee validity.

In practice, a test of a relatively changeable psychological construct such as suicide ideation, may be valid (i.e., accurate), but not particularly reliable over time (because suicide ideation is likely to fluctuate).

Image source: http://www.socialresearchmethods.net/kb/relandval.phpReliability is about the consistency of a measure. Validity is about whether a test actually measures what its meant to measure.Validity requires reliability, but reliability alone is not sufficient for validity.See the more complex discussion of this at:http://en.wikiversity.org/wiki/Reliability_and_validity

Reliability and validity
(Howitt & Cramer, 2005)

Reliability and validity are not inherent characteristics of measures. They are affected by the context and purpose of the measurement a measure that is valid for one purpose may not be valid for another purpose.


Reliability

Reproducibility of a measurement

Image source: UnknownReliable; not validReliable; valid

Types of reliability

Internal consistency: Correlation among multiple items in a factorSplit-half reliability

Odd-even reliability

Cronbachs Alpha ()

Test-retest reliability: Correlation between test at one time and anotherProduct-moment correlation (r)

Inter-rater reliability: Correlation between one observer and another:Kappa


Reliability rule of thumb

.95 = may be overly reliable or redundant this is subjective and whether a scale is overly reliable depends also on the nature what is being measured

Image source: http://commons.wikimedia.org/wiki/File:Circle-Thumb.pngImage author: Acadac, http://commons.wikimedia.org/wiki/User:AcadacImage license: Public domain

Reliability rule of thumb

Table 7 Fabrigar et al (1999).

Table 7 Fabrigar et al. (1999)

Rule of thumb - reliability coefficients should be over .70, up to approx. .90

Image source: Table 7 Fabrigar et al (1999).

Internal consistency
(or internal reliability)

Internal consistency refers to:How well multiple items combine as a measure of a single concept

The extent to which responses to multiple items are consistent with one another

Internal consistency can measured by:Split-half reliability


Cronbach's alpha

Internal consistency
(Recoding)

Remember to:Ensure that negatively-worded items are recoded

Types of internal consistency:
Split-half reliability

Sum the first half of the items.

Sum the second half of the items.

Compute a correlation between the sums of the two halves.

ReferenceHowitt & Cramer (2005), p. 221

Types of internal consistency -

Sum items 1, 3, 5, etc.

Sum items 2, 4, 6, etc.

Compute a correlation between the sums of the two halves.


Types of internal reliability:
Alpha reliability (Cronbach's )

Averages all possible split-half reliability coefficients.

Akin to a single score which represents the degree of intercorrelation amongst the items.


More items greater reliability
(The more items, the more rounded the measure)

Law of diminishing returns

Min. = 2?

Max. = unlimited?

Typically ~ 4 to 12 items per factor

Final decision is subjective and depends on research context

How many items per factor?

Internal reliability example:
Student-rated
quality of maths teaching

10-item scale measuring students assessment of the educational quality of their maths classes

4-point Likert scale ranging from:
strongly disagree to strongly agree

This example is from Francis 6.1

Quality of mathematics teaching

My maths teacher is friendly and cares about me

The work we do in our maths class is well organised.

My maths teacher expects high standards of work from everyone.

My maths teacher helps me to learn.

I enjoy the work I do in maths classes.

+ 5 more

Internal reliability example: Quality of maths teaching

Image source: James Neill, Creative Commons Attribution-Share Alike 2.5 Australia, http://creativecommons.org/licenses/by-sa/2.5/au/SPSS Analyze Scale Reliability Analysis

SPSS:
Corrected Item-total correlation


SPSS: Cronbachs


Item-total Statistics

Scale Scale CorrectedMean Variance Item- Alphaif Item if Item Total if ItemDeleted Deleted Correlation Deleted

MATHS1 25.2749 25.5752 .6614 .8629MATHS2 25.0333 26.5322 .6235 .8661MATHS3 25.0192 30.5174 .0996 .9021MATHS4 24.9786 25.8671 .7255 .8589MATHS5 25.4664 25.6455 .6707 .8622MATHS6 25.0813 24.9830 .7114 .8587MATHS7 25.0909 26.4215 .6208 .8662MATHS8 25.8699 25.7345 .6513 .8637MATHS9 25.0340 26.1201 .6762 .8623MATHS10 25.4642 25.7578 .6495 .8638

Reliability Coefficients

N of Cases = 1353.0 N of Items = 10

Alpha = .8790

SPSS: Reliability output

Item-total Statistics

Scale Scale CorrectedMean Variance Item- Alphaif Item if Item Total if ItemDeleted Deleted Correlation Deleted

MATHS1 22.2694 24.0699 .6821 .8907MATHS2 22.0280 25.2710 .6078 .8961MATHS4 21.9727 24.4372 .7365 .8871MATHS5 22.4605 24.2235 .6801 .8909MATHS6 22.0753 23.5423 .7255 .8873MATHS7 22.0849 25.0777 .6166 .8955MATHS8 22.8642 24.3449 .6562 .8927MATHS9 22.0280 24.5812 .7015 .8895MATHS10 22.4590 24.3859 .6524 .8930

Reliability Coefficients

N of Cases = 1355.0 N of Items = 9

Alpha = .9024

SPSS: Reliability output

Reliabilities: Example table

Image source: James Neill, Creative Commons Attribution-Share Alike 2.5 Australia, http://creativecommons.org/licenses/by-sa/2.5/au/Life Effectiveness Questionnaire - http://wilderdom.com/leq.html

Validity

Validity is the extent to which an instrument actually measures what it purports to measure.

Validity = does the test measure what its meant to measure?

Image source: UnknownReliable; validhttp://en.wikipedia.org/wiki/Construct_Validity

Validity

Validity is multifaceted and includes:Correlations with similar measures

How the measure performs in relation to other variables

How well the measure helps to predict the future

From http://www.socialresearchmethods.net/kb/measval.htm

Types of validity

Face validity

Content validity

Criterion validityConcurrent

Predictive

Construct validityConvergent

Discriminant

From http://www.socialresearchmethods.net/kb/measval.htm

Face validity
(low-level of importance overall)

Asks:
"Do the questions appear to measure what the test purports to measure?"

Important for:
Respondent buy-in

How assessed:
Read the test items

Adapted from: http://www.psyasia.com/supportsuite/index.php?_m=knowledgebase&_a=viewarticle&kbarticleid=85&nav=0Prima facie extent to which an item is judged to reflect target construct.e.g., a test of ability to add two-digit numbers should cover the full range of combinations of digits. A test with only one-digit numbers, or only even numbers, would (on the face of it) not have good coverage of the content domain.

Content validity
(next level of importance)

Asks:
"Are questions measuring the complete construct?"

Important for:
Ensuring holistic assessment

How assessed:
Diverse means of item generation (lit. review, theory, interviews, expert review)

Adapted from: http://www.psyasia.com/supportsuite/index.php?_m=knowledgebase&_a=viewarticle&kbarticleid=85&nav=0Extent to which test content covers a representative sample of the domain to be measured. Compare test with: existing literature

expert panels

qualitative interviews / focus groups with target sample

Criterion validity
(high importance)

Asks:
"Can a test score predict real world outcomes?"

Important for:
Test relevance and usefulness

How assessed:
Concurrent validity: Correlate test scores with recognised external criteria such as performance appraisal scores

Predictive validity: Correlate test scores with future outcome e.g., offender risk rating with recidivism

Adapted from: http://www.psyasia.com/supportsuite/index.php?_m=knowledgebase&_a=viewarticle&kbarticleid=85&nav=0Involves the correlation between the test and a criterion variable (or variables) taken as representative of the construct.

Concurrent validityCorrelation between the measure and other recognised measures of the target construct

Predictive validityExtent to which a measure predicts something that it theoretically should be able to predict.

Criterion refers to the true score. Criterion validity refer to measures of relationship between the criterion variable and the measured variable.http://en.wikipedia.org/wiki/Concurrent_validityhttp://www.socialresearchmethods.net/kb/measval.htm

Construct validity
(high importance)

Asks:
Does the test assess the construct it purports to? ("the truth, the whole truth and nothing but the truth.")

Important for:
Making inferences from operationalisations to theoretical constructs

How assessed:

- Theoretical (is the theory about the construct valid?)- StatisticalConvergent correlation with similar measures;Divergent not correlated with other constructs

Adapted from: http://www.psyasia.com/supportsuite/index.php?_m=knowledgebase&_a=viewarticle&kbarticleid=85&nav=0Totality of evidence about whether a particular operationalisation of a construct adequately represents what is intended.

Not distinct from support for the substantive theory of the construct.

Involves empirical and theoretical support.

Includes:Statistical analyses of the internal structure of the test including the relationships between responses to different test items (internal consistency).

Relationships between the test and measures of other constructs.

Convergent validityExtent to which a measure correlates with measures with which it theoretically should be associated.

Discriminant validityExtent to which a measure does not correlate with measures with which it theoretically should not be associated.

Construct validity
(high importance)

Image source:http://www.socialresearchmethods.net/kb/considea.phpImage license: 2006, William M.K. Trochim, All Rights Reserved

Composite Scores


Image source: http://www.flickr.com/photos/generated/2585844966/By generated http://www.flickr.com/photos/generated/License: Creative Commons by Attribution 2.0 http://creativecommons.org/licenses/by/2.0/deed.en

Composite scores
(Factor scores)

Combine item-scores into overall scores which represent individual differences in the target constructs.These new 'continuous' variables can then be used for:Descriptive statistics and histograms

Correlations

As IVs and/or DVs in inferential analyses such as MLR and ANOVA

Composite scores
(Factor scores)

There are two ways of creating composite scores:Unit weighting

Regression weighting

Unit weighting

Average (or total) of item scores within a factor.
(each variable is equally weighted)

X = mean(y1yp)

Unit Weighting

.25.25.25.25

Creating composite scores: Dealing with missing data

It can be helpful to maximise sample size by allowing for some missing data.

Composite scores:
Missing data

SPSS syntax:Compute X = mean (v1, v2, v3, v4, v5, v6)Compute X = mean.4 (v1, v2, v3, v4, v5, v6)Specify a min. # of items. If the min. isn't available, the composite score will be missingHow many items can be missed? Depends on overall reliability. A rule of thumb:Allow 1 missing per 4 to 5 items

Allow 2 missing per 6 to 8 items

Allow 3+ missing per 9+ items

A researcher may decide to be more or less conservative depending on the factors reliability, sample size, and the nature of the study.

compute X = mean (v1, v2, v3, v4, v5.v6)
(note: a missing value will be returned for a case if 1 or more of its items are missing data)compute X = mean.4 (v1, v2, v3, v4, v5,v6)
(note: in this case a missing value will be returned for a case if 3 or more of its items are missing data the .4 means calculate a mean using data from 4 or more items, otherwise make X missing)Image source: http://commons.wikimedia.org/wiki/File:Circle-Thumb.pngImage author: Acadac, http://commons.wikimedia.org/wiki/User:AcadacImage license: Public domain


Factor score regression weighting
The contribution of each
item to the composite score
is weighted to reflect some
items more than other
items.X = 20*a + .19*b + .27*c + .34*d

X

.20.19.27.34a

b

c

d

This is arguably more valid, but the advantage may be marginal, and it makes factor scores difficult to compare.

X = r1y1 +. + rpypRegression weighting will also include small weights for non-target items


Two calculation methods:Manual (use Compute)

Automatic (use Factor Analysis Factor Scores)

Image source: James Neill, Creative Commons Attribution-Share Alike 2.5 Australia, http://creativecommons.org/licenses/by-sa/2.5/au/

Regression weighting SPSS output

Data view

Variable viewImage sources: James Neill, Creative Commons Attribution-Share Alike 2.5 Australia, http://creativecommons.org/licenses/by-sa/2.5/au/

Writing up
instrument development


Image source: http://www.flickr.com/photos/mezone/21970578/By http://www.flickr.com/photos/mezone/License: Creative Commons by Attribution 2.0 http://creativecommons.org/licenses/by/2.0/deed.en


IntroductionLiterature review about underlying factors theory and research

MethodMaterials/Instrumentation summarise how the measures were developed and the expected factor structure
e.g., present a table of the expected factors and their operational definitions.


ResultsFactor analysisAssumption testing

Extraction method & rotation

# of factors & items removed

Names & definitions of factors

Item factor loadings & communalities

Factor correlations

Reliability

Composite scores

Factor correlations


DiscussionTheoretical underpinning Was it supported by the data? What adaptations should be made to the theory?

Quality / usefulness of measure Provide an objective, critical assessment, reflecting the measures' strengths and weaknesses

Recommendations for further improvement

Writing up a factor analysisSee downloadable example

Summary

Operationally define concepts

Brainstorm measurement items

Draft measure aiming to minimise measurement error

Pre-test & pilot

Use EFA, reliability, and validity

Create composite scores


Allen, P. & Bennett, K. (2008). Reliability analysis (Ch 15) in SPSS for the health & behavioural sciences (pp. 205-218). South Melbourne, Victoria, Australia: Thomson.

Bryman, A. & Cramer, D. (1997). Concepts and their measurement (Ch. 4). In Quantitative data analysis with SPSS for Windows: A guide for social scientists (pp. 53-68). Routledge.

DeCoster, J. (2000). Scale construction notes. http://www.stat-help.com/scale.pdf (pdf)

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299.

Fowler, F. (2002). Designing questions to be good measures. In Survey research methods (3rd ed.)(pp. 76-103). Thousand Oaks, CA: Sage. Ereserve.

Howitt, D. & Cramer, D. (2005). Reliability and validity: Evaluating the value of tests and measures (Ch. 13). In Introduction to research methods in psychology (pp. 218-231). Harlow, Essex: Pearson. eReserve.

References

Open Office Impress

This presentation was made using Open Office Impress.

Free and open source software.

http://www.openoffice.org/product/impress.html

Click to edit the title text format

Click to edit the outline text formatSecond Outline LevelThird Outline LevelFourth Outline LevelFifth Outline LevelSixth Outline LevelSeventh Outline LevelEighth Outline LevelNinth Outline Level








psychometric-instrument-development-reliabilities-and-composite-scores-1206978537846832-2.odp

Documents

Transcript of psychometric-instrument-development-reliabilities-and-composite-scores-1206978537846832-2.odp