Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models

by: Anders Skrondal, Sophia Rabe-Hesketh

(06 May 2004)

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Abstract

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. Contact Editor: Calver OTI #1: C1830

@book{citeulike:975342, abstract = {{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. Contact Editor: Calver OTI \#1: C1830}}, author = {Skrondal, Anders and Rabe-Hesketh, Sophia }, citeulike-article-id = {975342}, howpublished = {Hardcover}, isbn = {1584880007}, keywords = {equation, framework, latent, modelling, structural, teaching, variable}, month = {May}, posted-at = {2008-04-25 16:54:00}, priority = {5}, publisher = {{Chapman \& Hall/CRC}}, title = {Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models}, url = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&path=ASIN/1584880007}, year = {2004} }

RIS record

TY - BOOK ID - citeulike:975342 L3 - citeulike-article-id:975342 TI - Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models PB - {Chapman & Hall/CRC} SN - 1584880007 N2 - {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. Contact Editor: Calver OTI #1: C1830} KW - equation KW - framework KW - latent KW - modelling KW - structural KW - teaching KW - variable AU - Skrondal, Anders AU - Rabe-Hesketh, Sophia PY - 2004/05/06/ UR - http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/1584880007 ER -

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