Generalized latent variable modeling : multilevel, longitudinal, and structural equation models
Author(s)
Bibliographic Information
Generalized latent variable modeling : multilevel, longitudinal, and structural equation models
（Interdisciplinary statistics）
Chapman & Hall/CRC, c2004
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Note
Includes bibliographical references (p. 445-486) and indexes
Description and Table of Contents
Description
This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models. Following a gentle introduction to latent variable modeling, the authors clearly explain and contrast a wide range of estimation and prediction methods from biostatistics, psychometrics, econometrics, and statistics. They present exciting and realistic applications that demonstrate how researchers can use latent variable modeling to solve concrete problems in areas as diverse as medicine, economics, and psychology. The examples considered include many nonstandard response types, such as ordinal, nominal, count, and survival data. Joint modeling of mixed responses, such as survival and longitudinal data, is also illustrated. Numerous displays, figures, and graphs make the text vivid and easy to read.
About the authors:
Anders Skrondal is Professor and Chair in Social Statistics, Department of Statistics, London School of Economics, UK
Sophia Rabe-Hesketh is a Professor of Educational Statistics at the Graduate School of Education and Graduate Group in Biostatistics, University of California, Berkeley, USA.
Table of Contents
METHODOLOGY: The Omni-Presence of Latent Variables. Modeling Different Response Processes. Classical Latent Variable Models. General Model Framework. Identification and Equivalence. Estimation. Assigning Values to Latent Variables. Model Specification and Inference. APPLICATIONS: Dichotomous Responses. Ordinal Responses. Counts. Durations and Survival. Comparative Responses. Multiple Processes and Mixed Responses.
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