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 Head of the Biostatistics Group, Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway.Sophia Rabe-Hesketh is a Professor in the area of Educational Statistics at the Graduate School of Education, University of California, Berkeley, USA.FeaturesUnifies different modeling traditions, integrating and synthesizing models from the statistical, biostatistical, econometric, and psychometric literaturesGives a thorough overview of estimation and prediction methods for latent variable models, providing a guide to the various techniques availableApplies to a wide range of disciplines, including medicine, biology, sociology, psychology, and economicsContains many examples worked out in great detailProvides software, datasets, and scripts for some of the applications discussed at www.gllamm.org/books Copyright (C) Muze Inc. 2005. For personal use only. All rights reserved.
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