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 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. y 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 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. eneralized 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 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. mp;lt;br>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 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 Co.