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PREFACE    Measurement and Scaling   Definition of a test  Measurement and scaling    Classical Test Theory  True score and measurement error   The population of persons     Classical Test Theory and Reliability  The definition of reliability and the standard error of measurement  The definition of parallel tests  Reliability and test length   Reliability and group homogeneity  Estimating the true score  Correction for attenuation    Estimating Reliability  Reliability estimation from a single administration of a test  Reliability estimation with parallel tests   Reliability estimation with the test?retest method  Reliability and factor analysis  Score profiles and estimation of true scores  Reliability and conditional errors of measurement    Generalizability Theory  Basic concepts of G theory   One-facet designs, the p × i design, and the i : p design  The two-facet crossed p × i × j design  An example of a two-facet crossed p × i × j design: The generalizability of job performance measurements  The two-facet nested p × (i : j) design  Other two-facet designs  Fixed facets   Kinds of measurement errors   Conditional error variance  Concluding remarks    Models for Dichotomous Items  The binomial model  The generalized binomial model  The generalized binomial model and item response models  Item analysis and item selection    Validity and Validation of Tests   Validity and its sources of evidence  Selection effects in validation studies  Validity and classification  Selection and classification with more than one predictor  Convergent and discriminant validation: A strategy for evidence-based validity  Validation and IRT  Research validity: Validity in empirical behavioral research    Principal Component Analysis, Factor Analysis, and Structural Equation Modeling: A Very Brief Introduction  Principal component analysis (PCA)  Exploratory factor analysis  Confirmatory factor analysis and structural equation modeling    Item Response Models  Basic concepts  The multivariate normal distribution and polytomous items  Item-test regression and item response models  Estimation of item parameters  Joint maximum likelihood estimation for item and person parameters  Joint maximum likelihood estimation and the Rasch model   Marginal maximum likelihood estimation  Markov chain Monte Carlo  Conditional maximum likelihood estimation in the Rasch model  More on the estimation of item parameters  Maximum likelihood estimation of person parameters  Bayesian estimation of person parameters   Test and item information  Model-data fit  Appendix: Maximum likelihood estimation of θ in the Rasch model    Applications of Item Response Theory  Item analysis and test construction  Test construction and test development  Item bias or DIF   Deviant answer patterns  Computerized adaptive testing (CAT)  IRT and the measurement of change  Concluding remarks    Test Equating  Some basic data collection designs for equating studies  The equipercentile method  Linear equating  Linear equating with an anchor test  A synthesis of observed score equating approaches: The Kernel method   IRT models for equating  Concluding remarks    Answers  References  Index    Each chapter contains an Introduction and Exercises.

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