Statistical Test Theory for the Behavioral Sciences

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ISBN: 9781584889588 | 1584889586
Cover: Hardcover
Copyright: 8/31/2007

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This book provides both a broad overview and a critical survey of assorted testing theories and models used in psychology, education, and other behavioral science fields. It explores the relationships and differences between several theories-including classical test, generalizability, and item response-and their applications. The authors present recent developments in testing, such as computerized adaptive testing, and use real data from the areas of psychology and education. The text also includes exercises at the end of each chapter with solutions in an appendix.

Measurement and Scaling	p. 1
Introduction	p. 1
Definition of a test	p. 1
Measurement and scaling	p. 2
Exercises	p. 7
Classical Test Theory	p. 9
Introduction	p. 9
True score and measurement error	p. 9
The population of persons	p. 12
Exercises	p. 14
Classical Test Theory and Reliability	p. 15
Introduction	p. 15
The definition of reliability and the standard error of measurement	p. 15
The definition of parallel tests	p. 17
Reliability and test length	p. 19
Reliability and group homogeneity	p. 20
Estimating the true score	p. 21
Correction for attenuation	p. 23
Exercises	p. 23
Estimating Reliability	p. 25
Introduction	p. 25
Reliability estimation from a single administration of a test	p. 26
Reliability estimation with parallel tests	p. 36
Reliability estimation with the test-retest method	p. 36
Reliability and factor analysis	p. 37
Score profiles and estimation of true scores	p. 37
Reliability and conditional errors of measurement	p. 42
Exercises	p. 44
Generalizability Theory	p. 47
Introduction	p. 47
Basic concepts of G theory	p. 48
One-facet designs, the p x i design and the i : p design	p. 50
The crossed design	p. 50
The nested i : p design	p. 54
The two-facet crossed p x i x j design	p. 55
An example of a two-facet crossed p x i x j design: The generalizability of job performance measurements	p. 59
The two-facet nested p x (i : j) design	p. 60
Other two-facet designs	p. 62
Fixed facets	p. 64
Kinds of measurement errors	p. 67
Conditional error variance	p. 73
Concluding remarks	p. 74
Exercises	p. 75
Models for Dichotomous Items	p. 79
Introduction	p. 79
The binomial model	p. 80
The binomial model in a homogeneous item domain	p. 82
The binomial model in a heterogeneous item domain	p. 87
The generalized binomial model	p. 88
The generalized binomial model and item response models	p. 91
Item analysis and item selection	p. 92
Exercises	p. 98
Validity and Validation of Tests	p. 101
Introduction	p. 101
Validity and its sources of evidence	p. 103
Selection effects in validation studies	p. 106
Validity and classification	p. 108
Selection and classification with more than one predictor	p. 115
Convergent and discriminant validation: A strategy for evidence-based validity	p. 118
The multitrait-multimethod approach	p. 119
Validation and IRT	p. 121
Research validity: Validity in empirical behavioral research	p. 122
Exercises	p. 123
Principal Component Analysis, Factor Analysis, and Structural Equation Modeling: A Very Brief Introduction	p. 125
Introduction	p. 125
Principal component analysis (PCA)	p. 125
Exploratory factor analysis	p. 127
Confirmatory factor analysis and structural equation modeling	p. 130
Exercises	p. 132
Item Response Models	p. 133
Introduction	p. 133
Basic concepts	p. 134
The Rasch model	p. 135
Two- and three-parameter logistic models	p. 136
Other IRT models	p. 139
The multivariate normal distribution and polytomous items	p. 143
Item-test regression and item response models	p. 146
Estimation of item parameters	p. 148
Joint maximum likelihood estimation for item and person parameters	p. 150
Joint maximum likelihood estimation and the Rasch model	p. 151
Marginal maximum likelihood estimation	p. 153
Markov chain Monte Carlo	p. 154
Conditional maximum likelihood estimation in the Rasch model	p. 156
More on the estimation of item parameters	p. 157
Maximum likelihood estimation of person parameters	p. 160
Bayesian estimation of person parameters	p. 162
Test and item information	p. 162
Model-data fit	p. 167
Appendix: Maximum likelihood estimation of [theta] in the Kasch model	p. 170
Exercises	p. 174
Applications of Item Response Theory	p. 177
Introduction	p. 177
Item analysis and test construction	p. 179
Test construction and test development	p. 180
Item bias or DIF	p. 182
Deviant answer patterns	p. 189
Computerized adaptive testing (CAT)	p. 191
IRT and the measurement of change	p. 194
Concluding remarks	p. 195
Exercises	p. 197
Test Equating	p. 199
Introduction	p. 199
Some basic data collection designs for equating studies	p. 202
Design 1: Single-group design	p. 202
Design 2: Random-groups design	p. 203
Design 3: Anchor-test design	p. 203
The equipercentile method	p. 204
Linear equating	p. 207
Linear equating with an anchor test	p. 208
A synthesis of observed score equating approaches: The kernel method	p. 212
IRT models for equating	p. 212
The Rasch model	p. 213
The 2PL model	p. 214
The 3PL model	p. 215
Other models	p. 216
Concluding remarks	p. 216
Exercises	p. 219
Answers	p. 221
References	p. 235
Author Index	p. 255
Subject Index	p. 261
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Statistical Test Theory for the Behavioral Sciences

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