Statistical approaches to measurement invariance
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,...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
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New York :
Routledge
c2011.
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| Edición: | 1st ed |
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| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009798284006719 |
Tabla de Contenidos:
- 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
