Structural Equation Modeling: A Bayesian Approach / Edition 1

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Overview

***Winner of the 2008 Ziegel Prize for outstanding new book of the year***

Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples.

Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject’s recent advances.

  • Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results.
  • Discusses the Bayes factor and Deviance Information Criterion (DIC) for model comparison.
  • Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations.
  • Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology.
  • Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets.

Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science.

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Editorial Reviews

From the Publisher
"This book is a welcome addition to any library and should be a valuable resource for research and teaching." (Technometrics, August 2008)
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Product Details

Meet the Author

Sik-Yum Lee is a professor of statistics at the Chinese University of Hong Kong. He earned his Ph.D. in biostatistics at the University of California, Los Angeles, USA. He received a distinguished service award from the International Chinese Statistical Association, is a former president of the Hong Kong Statistical Society, and is an elected member of the International Statistical Institute and a Fellow of the American Statistical Association. He serves as Associate Editor for Psychometrika and Computational Statistics & Data Analysis, and as a member of the Editorial Board of British Journal of Mathematical and Statistical Psychology, Structural Equation Modeling, Handbook of Computing and Statistics with Applications and Chinese Journal of Medicine. his research interests are in structural equation models, latent variable models, Bayesian methods and statistical diagnostics. he is editor of Handbook of Latent Variable and Related Models and author of over 140 papers.

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Table of Contents

About the Author.

Preface.

Chapter 1. Introduction.

Chapter 2. Some Basic Structural Equation Models.

Chapter 3. Covariance Structure Analysis.

Chapter 4. Bayesian Estimation of Structural Equation Models.

Chapter 5. Model Comparison and Model Checking.

Chapter 6. Structural Equation Models with Continuous and Ordered Categorical Variables.

Chapter 7. Structural Equation Models with Dichotomous Variables.

Chapter 8. Nonlinear Structural Equation Models.

Chapter 9. Two-level Nonlinear Structural Equation Models.

Chapter 10. Multisample Analysis of Structural Equation Models.

Chapter 11. Finite Mixtures in Structural Equation Models.

Chapter 12. Structural Equation Models with Missing Data.

Chapter 13. Structural Equation Models with Exponential Family of Distributions.

Chapter 14. Conclusion.

Index.

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