Average Reviews:
(More customer reviews)This book provides well-written, comprehensive coverage of latent variable modeling. Suitable for a masters level (or preferably, a Ph.D.) statistician. Not suitable for someone with a few intro stat courses. Presents multilevel, longitudinal and structural equation modeling and factor analysis using a unified framework, which is both a help (better insights, easier to extend conceptually) and a hindrance (complex when one first tries to understand it). Includes many examples, which makes it much easier to apply these techniques to real life data analysis problems. Practically an encyclopedia of statistical models.
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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, a wide range of estimation and prediction methods from biostatistics, psychometrics, econometrics, and statistics are explained and contrasted in a simple way. Exciting and realistic applications demonstrate how researchers can use latent variable modeling to solve concrete problems in areas as diverse as medicine, economics, and psychology. Many nonstandard response types are considered including 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.
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