Table of contents for Structural equation modeling : a Bayesian approach / Sik-Yum Lee.

Bibliographic record and links to related information available from the Library of Congress catalog.

Note: Contents data are machine generated based on pre-publication provided by the publisher. Contents may have variations from the printed book or be incomplete or contain other coding.

Chapter 1	Introduction
1.1 Standard structural equation models
1.2 Covariance structure analysis
1.3 Why a new book?
1.4 Objectives of the book
1.5 Data sets and notations
Appendix 1.1	
Chapter 2	Some Basic Structural Equation Models
2.1	Introduction
2.2	Exploratory factor analysis
2.2.1	Model definition
2.2.2	Identification and analysis of the model
2.3	Confirmatory and higher-order factor analysis models
2.3.1	Confirmatory factor analysis model
2.3.2	Estimation of factor scores
2.3.3	Higher-order factor analysis model
2.4	The LISREL model
2.5.	The Bentler-Weeks model
2.6.	Discussion
Chapter 3	Covariance Structure Analysis
3.1	Introduction
3.2	Definition, notations and preliminary results
3.3	GLS analysis of covariance structure
3.3.1	The GLS approach
3.3.2	Asymptotic properties of the GLS estimator
3.4	ML analysis of covariance structure
3.5	Asymptotically distribution-free methods
3.6	Some iterative procedures
3.6.1	Numerical examples
Appendix 3.1	Matrix calculus
Appendix 3.2	Some basic results in probability theory
Appendix 3.3	
Chapter 4	Bayesian Estimation of Structural Equation Models
4.1	Introduction
4.2	Basic principles and concepts of Bayesian estimation
4.2.1	Bayesian estimation
4.2.2	Prior distributions
4.2.3	Posterior analysis
4.3	Bayesian estimation of the CFA model
4.3.1	Conditional distributions
4.3.2	A numerical example
4.3.3	Robustness to small sample sizes
4.4	Bayesian estimation of standard SEMs
4.5	Bayesian estimation via WinBUGS
Appendix 4.1	The Metropolis-Hastings algorithm
Appendix 4.2	
Appendix 4.3	
Chapter 5	Model Comparison and Model Checking
5.1	Introduction
5.2	Bayes factor
5.3	Path sampling
5.4	An application: Bayesian analysis of SEMs with fixed covariates
5.5	Other methods
5.5.1	Bayesian information criterion and Akaike information criterion
5.5.2	Deviance information criterion
5.5.3	Posterior predictive p-value
5.5.4	Residuals and outlier analysis
5.6	Discussion
Appendix 5.1	
Appendix 5.2	
Appendix 5.3	
Chapter 6	Structural Equation Models with Continuous and Ordered Categorical Variables
6.1	Introduction
6.2	The basic model
6.3	Bayesian estimation and goodness-of-fit
6.3.1	Conditional distribution
6.3.2	Implementation
6.3.3	Bayesian estimates
6.3.4	Goodness-of-fit of the model
6.3.5	An illustrative example
6.4	Bayesian model comparison
6.5	Application 1: Bayesian selection of the number of factors in EFA
6.5.1	A simulation study
6.5.2	A real example
	6.6	Application 2: Bayesian analysis of quality of life data
	6.6.1	A synthetic illustrative example
	6.6.2	Application of WinBUGS
Chapter 7	Structural Equation Models with Dichotomous Variables
7.1 Introduction
7.2 Bayesian analysis
7.2.1 Illustrative example 1
7.3 Analysis of a multivariate probit confirmatory factor analysis model
7.3.1 Illustrative example 2
 7.4 Discussion
	Appendix 7.1	
Chapter 8	Nonlinear Structural Equation Models
8.1 Introduction
8.2 Bayesian analysis of a nonlinear SEM
8.2.1 The model
8.2.2 The Gibbs sampler for posterior simulation
8.2.3 Full conditional distributions
8.2.4 Bayesian estimates
8.2.5 Illustrative example 1
8.2.6 Comparison with a product indicator method
8.2.7 Comparison with the LMS approach via the Kenny-Judd model
8.3 Bayesian estimation of nonlinear SEMs with mixed continuous and ordered categorical variables
8.3.1 Posterior analysis
8.3.2 Illustrative Example 2
8.4 Bayesian estimation of SEMs with nonlinear covariates and latent variables
8.4.1 A model with ordered categorical variables, and nonlinear covariates and latent variables
8.4.2 Illustrative example 3
8.4.3 Analysis of an artificial example using WinBUGS
8.5 Bayesian model comparison
8.5.1 Path sampling procedure for computing the Bayes factor
8.5.2 Illustrative example 4
8.5.3 Model comparison using DIC and WinBUGS
8.5.4 Remarks on goodness-of-fit assessment
Chapter 9	Two-level Nonlinear Structural Equation Models
9.1 Introduction
9.2 A two-level nonlinear SEM with mixed type variables
9.3 Bayesian estimation
9.3.1 Posterior simulation and Bayesian estimates
9.3.2 Simulation studies
9.4 Goodness-of-fit and model comparison
9.5 An application: Filipina CSWs study
9.6 Two-level nonlinear SEMs with cross level effects
9.6.1 The model
9.6.2 Bayesian analysis
9.6.3 An application
9.7 Analysis of two-level nonlinear SEMs using WinBUGS
Appendix 9.1	
Appendix 9.2	
Appendix 9.3	
Appendix 9.4	
Appendix 9.5	
Appendix 9.6	
Chapter 10	Multisample Analysis of Structural Equation Models
10.1 Introduction
10.2 The multisample nonlinear structural equation model
10.3 Bayesian analysis of multisample nonlinear SEMs
10.3.1 Bayesian estimation
10.3.2 Bayesian model comparison
10.4 Numerical Illustrations
10.4.1 Analysis of multisample management data
10.4.2 Analysis of multisample quality of life data via WinBUGS
Appendix 10.1	
Chapter 11	Finite Mixtures in Structural Equation Models
11.1 Introduction
11.2 Finite mixtures in SEMs
11.3 Bayesian estimation and classification
11.4 Examples and simulation study
11.4.1 Analysis of an artificial example 
11.4.2 A simulation study
11.4.3 An example on ?Job? and ?Home life?
11.5 Bayesian model selection of mixture SEMs
11.5.1 A model selection procedure through path sampling
11.5.2 A simulation study
11.5.3 An illustrative example
Appendix 11.1	
Appendix 11.2	
Chapter 12	Structural Equation Models with Missing Data
12.1 Introduction
12.2 A general framework for SEM with missing data that are MAR
12.3 Nonlinear SEM with missing continuous and ordered categorical data
12.3.1 A simulation study
12.3.2 An illustrative example
12.4 Mixture of SEMs with missing data
12.5 Nonlinear SEMs with nonignorable missing data
12.5.1 The model and the nonignorable missing mechanism
12.5.2 Bayesian analysis of the model
12.5.3 An illustrative real example
12.6 Analaysis of SEMs with missing data via WinBUGS
Appendix 12.1	
Chapter 13	Structural Equation Models with Exponential Family of Distributions
13.1 Introduction
13.2 The SEM framework with exponential family of distributions
13.2.1 The model
13.2.2 The nonignorable missing mechanism
13.3 A Bayesian approach
13.3.1 Prior distributions
13.3.2 Full conditional distributions
13.3.3 Model comparison
13.4 A simulation study
13.5 A real example: A compliance study of patients
13.6 Bayesian analysis of an artificial example using WinBUGS
13.7 Discussion
Appendix 13.1	
Appendix 13.2	
Chapter 14	Conclusion

Library of Congress Subject Headings for this publication:

Structural equation modeling.
Bayesian statistical decision theory.