Applied Latent Class Analysis
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Applied Latent Class Analysis presents new techniques for the analysis of categorical data - one of the fastest growing areas in applied statistics. These techniques handle data where one cannot compute meaningful averages (for example, nation of origin, religion, type of disease, political party identification). The essays, written by leading figures in the field, introduce innovative approaches to these data and illustrate practical applications.Table of contents:1. Latent class analysis: the empirical study of latent types, latent variables, and latent structures, and some notes on the hist...
Applied Latent Class Analysis presents new techniques for the analysis of categorical data - one of the fastest growing areas in applied statistics. These techniques handle data where one cannot compute meaningful averages (for example, nation of origin, religion, type of disease, political party identification). The essays, written by leading figures in the field, introduce innovative approaches to these data and illustrate practical applications.
Table of contents:
1. Latent class analysis: the empirical study of latent types, latent variables, and latent structures, and some notes on the history of this subject Leo A. Goodman; 2. Basic concepts and procedures in single and multiple group latent class analysis Allan L. McCutcheon; Part I. Classification and Measurement: 3. Latent class cluster analysis Jeroen K. Vermunt and Jay Magidson; 4. Some examples of latent budget analysis and its extensions Peter G. M. van der Heijden, Andries van der Ark and Ab Mooijaart; 5. Ordering the classes Marcel Croon; 6. Comparison and choice: analyzing discrete preference data by latent class scaling models Ulf Böckenholt; 7. Three-parameter linear logistic latent class analysis Anton K. Formann and Thomas Kohlmann; Part II. Causal Analysis and Dynamic Models: 8. Use of categorical and continuous covariates in latent class analysis C. Mitchell Dayton and George B. Macready; 9. Directed loglinear modeling with latent variables: causal models for categorical data with nonsystematic and systematic measurement errors Jacques A. Hagenaars; 10. Latent class models for longitudinal data Linda M. Collins and Brian P. Flaherty; 11. Latent Markov chains Rolf Langeheine and Frank van der Pol; Part III. Unobserved Heterogeneity and Nonresponse: 12. A latent class approach for measuring the fit of a statistical model Tamás Rudas; 13. Mixture regression models Michel Wedel and Wayne S. DeSarbo; 14. A general latent class approach to unobserved heterogeneity in the analysis of event history data Jeroen K. Vermunt; 15. Latent class models for contingency tables with missing data Christopher Winship, Robert D. Mare, and John Robert Warren.
Applied Latent Class Analysis introduces several recent innovations in latent class analysis to a wider audience of researchers. Many of the world's leading innovators in the field of latent class analysis have contributed essays to this volume, each presenting a key innovation to the basic LCM.
Applied Latent Class Analysis introduces several recent innovations in latent class analysis to a wider audience of researchers.
Table of contents:
1. Latent class analysis: the empirical study of latent types, latent variables, and latent structures, and some notes on the history of this subject Leo A. Goodman; 2. Basic concepts and procedures in single and multiple group latent class analysis Allan L. McCutcheon; Part I. Classification and Measurement: 3. Latent class cluster analysis Jeroen K. Vermunt and Jay Magidson; 4. Some examples of latent budget analysis and its extensions Peter G. M. van der Heijden, Andries van der Ark and Ab Mooijaart; 5. Ordering the classes Marcel Croon; 6. Comparison and choice: analyzing discrete preference data by latent class scaling models Ulf Böckenholt; 7. Three-parameter linear logistic latent class analysis Anton K. Formann and Thomas Kohlmann; Part II. Causal Analysis and Dynamic Models: 8. Use of categorical and continuous covariates in latent class analysis C. Mitchell Dayton and George B. Macready; 9. Directed loglinear modeling with latent variables: causal models for categorical data with nonsystematic and systematic measurement errors Jacques A. Hagenaars; 10. Latent class models for longitudinal data Linda M. Collins and Brian P. Flaherty; 11. Latent Markov chains Rolf Langeheine and Frank van der Pol; Part III. Unobserved Heterogeneity and Nonresponse: 12. A latent class approach for measuring the fit of a statistical model Tamás Rudas; 13. Mixture regression models Michel Wedel and Wayne S. DeSarbo; 14. A general latent class approach to unobserved heterogeneity in the analysis of event history data Jeroen K. Vermunt; 15. Latent class models for contingency tables with missing data Christopher Winship, Robert D. Mare, and John Robert Warren.
Applied Latent Class Analysis introduces several recent innovations in latent class analysis to a wider audience of researchers. Many of the world's leading innovators in the field of latent class analysis have contributed essays to this volume, each presenting a key innovation to the basic LCM.
Applied Latent Class Analysis introduces several recent innovations in latent class analysis to a wider audience of researchers.