[学科前沿] Latent Class Analysis of Survey Error [推广有奖]

Combining theoretical, methodological, and practical aspects, Latent Class Analysis of Survey Error successfully guides readers through the accurate interpretation of survey results for quality evaluation and improvement. This book is a comprehensive resource on the key statistical tools and techniques employed during the modeling and estimation of classification errors, featuring a special focus on both latent class analysis (LCA) techniques and models for categorical data from complex sample surveys. Drawing from his extensive experience in the field of survey methodology, the author examines early models for survey measurement error and identifies their similarities and differences as well as their strengths and weaknesses.
                                                                       
Series: Wiley Series in Survey MethodologyTitle: Latent Class Analysis of Survey Error
Author:Paul P. Biemer
Hardcover: 387pages
Publisher: John Wiley & Sons
Language: English
ISBN: 97804702891148

Contents
Preface.
Abbreviations.
1. Survey Error Evaluation.
2. A General Model for Measurement Error.
3. Response Probability Models for Two Measurements.
4. Latent Class Models for Evaluating Classifi cation Errors.
5. Further Aspects of Latent Class Modeling.
6. Latent Class Models for Special Applications.
7. Latent Class Models for Panel Data.
8. Survey Error Evaluation: Past, Present, and Future.
Appendix A. Two-Stage Sampling Formulas.
Appendix B. Loglinear Modeling Essentials.
References.
Index.