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.
* Introduces the Bayesian approach to SEMs, including discussion on the selection of prior distributions, and data augmentation.
* Demonstrates how to utilize the recent powerful tools in statistical computing including, but not limited to, the Gibbs sampler, the Metropolis-Hasting algorithm, and path sampling for producing various statistical results such as Bayesian estimates and Bayesian model comparison statistics in the analysis of basic and advanced SEMs.
* Discusses the Bayes factor, Deviance Information Criterion (DIC), and $L_\nu$-measure for Bayesian model comparison.
* Introduces a number of important generalizations of SEMs, including multilevel and mixture SEMs, latent curve models and longitudinal SEMs, semiparametric SEMs and those with various types of discrete data, and nonparametric structural equations.
* Illustrates how to use the freely available software WinBUGS to produce the results.
* Provides numerous real examples for illustrating the theoretical concepts and computational procedures that are presented throughout the book.
Researchers and advanced level students in statistics, biostatistics, public health, business, education, psychology and social science will benefit from this book.
Autorentext
Xin-Yuan Song and Sik-Yum Lee, Department of Statistics, The Chinese University of Hong Kong
Klappentext
Basic and Advanced Bayesian Structural Equation Modeling
With Applications in the Medical and Behavioral Sciences
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.
- Introduces the Bayesian approach to SEMs, including discussion on the selection of prior distributions, and data augmentation.
- Demonstrates how to utilize the recent powerful tools in statistical computing including, but not limited to, the Gibbs sampler, the Metropolis–Hasting algorithm, and path sampling for producing various statistical results such as Bayesian estimates and Bayesian model comparison statistics in the analysis of basic and advanced SEMs.
- Discusses the Bayes factor, Deviance Information Criterion (DIC), and L_measure for Bayesian model comparison.
- Introduces a number of important generalizations of SEMs, including multilevel and mixture SEMs, latent curve models and longitudinal SEMs, semiparametric SEMs and those with various types of discrete data, and nonparametric structural equations.
- Illustrates how to use the freely available software WinBUGS to produce the results.
- Provides numerous real examples for illustrating the theoretical concepts and computational procedures that are presented throughout the book.
Researchers and advanced level students in statistics, biostatistics, public health, business, education, psychology and social science will benefit from this book.
Inhalt
About the authors xiii
Preface xv
1 Introduction 1
1.1 Observed and latent variables 1
1.2 Structural equation model 3
1.3 Objectives of the book 3
1.4 The Bayesian approach 4
1.5 Real data sets and notation 5
Appendix 1.1: Information on real data sets 7
References 14
2 Basic concepts and applications of structural equation models 16
2.1 Introduction 16
2.2 Linear SEMs 17
2.2.1 Measurement equation 18
2.2.2 Structural equation and one extension 19
2.2.3 Assumptions of linear SEMs 20
2.2.4 Model identification 21
2.2.5 Path diagram 22
2.3 SEMs with fixed covariates 23
2.3.1 The model 23
2.3.2 An artificial example 24
2.4 Nonlinear SEMs 25
2.4.1 Basic nonlinear SEMs 25
2.4.2 Nonlinear SEMs with fixed covariates 27
2.4.3 Remarks 29
2.5 Discussion and conclusions 29
References 33
3 Bayesian methods for estimating structural equation models 34
3.1 Introduction 34
3.2 Basic concepts of the Bayesian estimation and prior distributions 35
3.2.1 Prior distributions 36
3.2.2 Conjugate prior distributions in Bayesian analyses of SEMs 37
3.3 Posterior analysis using Markov chain Monte Carlo methods 40
3.4 Application of Markov chain Monte Carlo methods 43
3.5 Bayesian estimation via WinBUGS 45
Appendix 3.1: The gamma, inverted gamma, Wishart, and inverted Wishart distributions and their characteristics 53
Appendix 3.2: The MetropolisHastings algorithm 54
Appendix 3.3: Conditional distributions [|Y, ] and [|Y,] 55
Appendix 3.4: Conditional distributions [|Y, ] and [|Y,] in nonlinear SEMs with covariates 58
Appendix 3.5: WinBUGS code 60
Appendix 3.6: R2WinBUGS code 61
References 62
4 Bayesian model comparison and model checking 64
4.1 Introduction 64
4.2 Bayes factor 65
4.2.1 Path sampling 67
4.2.2 A simulation study 70
4.3 Other model comparison statistics 73
4.3.1 Bayesian information criterion and Akaike information criterion 73
4.3.2 Deviance information criterion 74
4.3.3 L-measure 75
4.4 Illustration 76
4.5 Goodness of fit and model checking methods 78
4.5.1 Posterior predictive p-value 78
4.5.2 Residual analysis 78
Appendix 4.1: WinBUGS code 80
Appendix 4.2: R code in Bayes factor example 81
Appendix 4.3: Posterior predictive p-value for model assessment 83
References 83
5 Practical structural equation models 86
5.1 Introduction 86
5.2 SEMs with continuous and ordered categorical variables 86
5.2.1 Introduction 86
5.2.2 The basic model 88
5.2.3 Bayesian analysis 90
5.2.4 Appl…