UID:
almahu_9947367657102882
Umfang:
1 online resource (458 p.)
Ausgabe:
1st ed.
ISBN:
1-280-96270-4
,
9786610962709
,
0-08-047126-9
Serie:
Handbook of computing and statistics with applications ; v. 1
Inhalt:
This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables.- Covers a wide class of important models- Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data- Includes illustrative examples with real data sets from business, education, medicine, public health and sociology.- Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.
Anmerkung:
Description based upon print version of record.
,
Front cover; Handbook of Latent Variable and Related Models; Copyright page; Handbook Series on Computing and Statistics with Applications; Preface; About the Authors; Contributors; Table of contents; Chapter 1.Covariance Structure Models for Maximal Reliability of Unit-Weighted Composites; 1. Proposed identification condition for factor models; 2. Reliability based on proposed parameterization; 3. Properties of the coefficient; 4. Illustration with exploratory factor analysis; 5. Reliability with general latent variable models; 6. Dimension-free and greatest lower bound reliability
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7. Reliability of weighted composites8. Selection of weights for maximal reliability; 9. Conclusions; Acknowledgements; References; Chapter 2. Advances in Analysis of Mean and Covariance Structure when Data are Incomplete; 1. Introduction; 2. Missing data mechanism; 3. Methods for handling missing data; 4. Simulation studies; 5. Sensitivity analysis for missing data mechanism; 6. SEM software for incomplete data; References; Chapter 3. Rotation Algorithms: From Beginning to End; 1. Introduction; 2. Factor analysis; 3. A parameterization for Lambda and Phi; 4. Reference structures
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5. Thurstone's graphical rotation method6. Early analytic oblique rotation methods; 7. Pairwise algorithms; 8. Analytic rotation methods: Orthogonal; 9. Direct analytic methods: Oblique; 10. Discussion; References; Chapter 4. Selection of Manifest Variables; 1. Introduction; 2. Manifest variable selection in factor analysis; 3. SEFA and examples with empirical data; 4. Variable selection with a model fit and reliability analysis; 5. Conclusion and final remarks; Acknowledgements; References; Chapter 5. Bayesian Analysis of Mixtures Structural Equation Models with Missing Data; 1. Introduction
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2. Model description3. Bayesian analysis of the models; 4. Simulation studies; 5. An illustrative example; 6. Analysis via WinBUGS; 7. Discussion; Acknowledgements; Appendix A. The permutation sampler; Appendix B. Searching for identifiability constraints; Appendix C. Manifest variables in the ICPSR example; References; Chapter 6. Local Influence Analysis for Latent Variable Models with Non-Ignorable Missing Responses; 1. Introduction; 2. Local influence of latent variable models with non-ignorable missing data; 3. Normal mixed effects model; 4. Generalized linear mixed model; 5. Conclusion
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Appendix AAppendix B; Appendix C; References; Chapter 7. Goodness-of-Fit Measures for Latent Variable Models for Binary Data; 1. Introduction; 2. Latent variable models for binary responses; 3. Goodness-of-fit tests for latent variable models for binary data; 4. Limited information statistics; 5. Test based on the log-odds ratio; 6. Simulations; 7. Conclusion; Acknowledgements; References; Chapter 8 Bayesian Structural Equation Modeling; 1. Introduction; 2. Structural equation models; 3. Bayesian approach; 4. Democratization and industrialization application; 5. Discussion and future research
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Appendix A. Prior specifications
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English
Weitere Ausg.:
ISBN 0-444-52044-9
Sprache:
Englisch
