Basic and advanced Bayesian structural equation modeling : with applications in the medical and behavioral sciences / Xin-Yuan Song and Sik-Yum Lee.

By: Song, Xin-YuanContributor(s): Lee, Sik-YumMaterial type: TextTextSeries: Wiley series in probability and statisticsPublication details: Chichester, West Sussex : John Wiley & Sons, 2012Description: 1 online resource (xvii, 367 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9781118358887; 1118358880; 9781118359433; 1118359437Subject(s): Bayesian statistical decision theory | Structural equation modeling | Mathematics | Bayesian statistical decision theory | Structural equation modelingGenre/Form: Electronic books.Additional physical formats: Print version:: Basic and advanced Bayesian structural equation modeling.DDC classification: 519.5/3 | 519.53 LOC classification: QA278.3Other classification: MAT029000 Online resources: Wiley Online Library
Contents:
Introduction -- Basic concepts and applications of structural equation models -- Bayesian methods for estimating structural equation models -- Bayesian model comparison and model checking -- Practical structural equation models -- Structural equation models with hierarchical and multisample data -- Mixture structural equation models -- Structural equation modeling for latent curve models -- Longitudinal structural equation models -- Semiparametric structural equation models with continuous variables -- Structural equation models with mixed continuous and unordered categorical variables -- Structural equation models with nonparametric structural equations -- Transformation structural equation models -- Conclusion.
Summary: "This book provides clear instructions to researchers on how to apply Structural Equation Models (SEMs) for analyzing the inter relationships between observed and latent variables. Basic and Advanced Bayesian Structural Equation Modeling introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered categorical data, multilevel data, mixture data, longitudinal data, highly non-normal data, as well as some of their combinations. In addition, Bayesian semiparametric SEMs to capture the true distribution of explanatory latent variables are introduced, whilst SEM with a nonparametric structural equation to assess unspecified functional relationships among latent variables are also explored. Statistical methodologies are developed using the Bayesian approach giving reliable results for small samples and allowing the use of prior information leading to better statistical results. Estimates of the parameters and model comparison statistics are obtained via powerful Markov Chain Monte Carlo methods in statistical computing."--Publisher's website.
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Includes bibliographical references and index.

Introduction -- Basic concepts and applications of structural equation models -- Bayesian methods for estimating structural equation models -- Bayesian model comparison and model checking -- Practical structural equation models -- Structural equation models with hierarchical and multisample data -- Mixture structural equation models -- Structural equation modeling for latent curve models -- Longitudinal structural equation models -- Semiparametric structural equation models with continuous variables -- Structural equation models with mixed continuous and unordered categorical variables -- Structural equation models with nonparametric structural equations -- Transformation structural equation models -- Conclusion.

"This book provides clear instructions to researchers on how to apply Structural Equation Models (SEMs) for analyzing the inter relationships between observed and latent variables. Basic and Advanced Bayesian Structural Equation Modeling introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered categorical data, multilevel data, mixture data, longitudinal data, highly non-normal data, as well as some of their combinations. In addition, Bayesian semiparametric SEMs to capture the true distribution of explanatory latent variables are introduced, whilst SEM with a nonparametric structural equation to assess unspecified functional relationships among latent variables are also explored. Statistical methodologies are developed using the Bayesian approach giving reliable results for small samples and allowing the use of prior information leading to better statistical results. Estimates of the parameters and model comparison statistics are obtained via powerful Markov Chain Monte Carlo methods in statistical computing."--Publisher's website.

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