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.

From the Back Cover
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.

Contents
About the Author xi
Preface xiii
1 Introduction 1
2 Some Basic Structural Equation Models 13
3 Covariance Structure Analysis 31
4 Bayesian Estimation of Structural Equation Models 67
5 Model Comparison and Model Checking 111
6 Structural Equation Models with Continuous and Ordered
Categorical Variables 139
7 Structural Equation Models with Dichotomous Variables 175
8 Nonlinear Structural Equation Models 195
9 Two-level Nonlinear Structural Equation Models 243
10 Multisample Analysis of Structural Equation Models 293
11 Finite Mixtures in Structural Equation Models 319
12 Structural Equation Models with Missing Data 355
13 Structural Equation Models with Exponential Family of
Distributions 393
14 Conclusion 421
References 425
Index 427 

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