Statistical approaches to measurement invariance / / Roger E. Millsap

• Record Nr.: UNINA9910813079503321
• Autore: Millsap Roger Ellis
• Pubbl/distr/stampa: New York, : Routledge, c2011
• ISBN: 1-136-76111-X; 1-136-76112-8; 0-203-82196-3
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (359 p.)
• Disciplina: 150.28/7
• Soggetti: Psychological tests; Psychology - Statistical methods; Psychometrics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and indexes.
• Nota di contenuto: Front Cover; Statistical Approaches to Measurement Invariance; Copyright Page; Contents; Preface; Acknowledgments; 1. Introduction; What Is Measurement Invariance?; Is Measurement Bias an Important Problem?; About This Book; 2. Latent Variable Models; General Features; Model Restrictions; Problems in Latent Variable Models; 3. Measurement Bias; Multiple Populations; Measurement Invariance; Dimensionality and Invariance; Conditioning on Observed Scores; Appendix; 4. The Factor Model and Factorial Invariance; The Common Factor Model in Multiple Populations; Identification; Estimation; Fit EvaluationInvariance Constraints; An Example; Appendix: Factorial Invariance and Selection; 5. Factor Analysis in Discrete Data; The Factor Model; Estimation; Tests of Invariance; An Example; 6. Item Response Theory: Models, Estimation, Fit Evaluation; Models; Estimation; Model Fit Evaluation; 7. Item Response Theory: Tests of Invariance; Forms of Bias; Likelihood-Ratio Tests; Wald Statistics; Parameter Linkage; Effect Size Measures; The DFIT Approach; An Example; 8. Observed Variable Methods; Dichotomous Item Methods; Polytomous Item Methods; Random Effects Models; SIBTEST; An Example; 9. Bias In Measurement and PredictionPredictive Bias; Prediction Within the Factor Analysis Model; General Latent Variable Models; Conclusion; ; References; Author Index; Subject Index
• Sommario/riassunto: This book reviews the statistical procedures used to detect measurement bias. Measurement bias is examined from a general latent variable perspective so as to accommodate different forms of testing in a variety of contexts including cognitive or clinical variables, attitudes, personality dimensions, or emotional states. Measurement models that underlie psychometric practice are described, including their strengths and limitations. Practical strategies and examples for dealing with bias detection are provided throughout.The book begins with an introduction to the general topic,