王济川、王小倩所著的《结构方程模型：Mplus与应用（英文版）》采用国际著名SEM软件Mplus，使用真实数据来演示各种常见的以及某些新近发展起来的较高级的结构方程模型，提供相应的Mplus程序，并详细解读程序输出结果。参照本书提供的例题和相应的计算机程序，读者便能自己实践各种SEM模型。

王济川、王小倩所著的《结构方程模型：Mplus与应用（英文版）》以通俗易懂的方式系统地阐述结构方程模型的基本概念和统计原理，侧重各种结构方程模型的实际应用。《结构方程模型：Mplus与应用（英文版）》采用国际著名SEM软件Mplus，使用真实数据来演示各种常见的以及某些新近发展起来的较高级的结构方程模型，提供相应的Mplus程序，并详细解读程序输出结果。参照《结构方程模型：Mplus与应用（英文版）》提供的例题和相应的计算机程序，读者便能自己实践各种SEM模型。

本书可作为大学社会科学及公共卫生学院研究生以及统计和生物统计专业本科生教材，也可作为相关学科的研究人员从事统计分析的工具书。

1 Introduction

 1.1 Modelformulation

1.1.1 Measurement model

1.1.2 Structuralmodel

1.1.3 Model formulation in equations

 1.2 Modelidentification

 1.3 Modelestimation

 1.4 Modelevaluation

 1.5 Modelmodification

 1.6 Computer programs for SEM

 Appendix 1.A Expressing variances and covariances among observed variables as functions of model parameters

 Appendix 1.B Maximum likelihood function for SEM

2 Confirmatory factor analysis

 2.1 Basics ofCFA model

 2.2 CFA model with continuous indicators

 2.3 CFA model with non-normal and censoredcontinuous indicators

2.3.1 Testingnon-normality

2.3.2 CFA model with non-normalindicators

2.3.3 CFA model with censored data

 2.4 CFA model with categoricalindicators

2.4.1 CFAmodelwithbinaryindicators

2.4.2 CFA model with ordered categoricalindicators

 2.5 Higher order CFA model

 Appendix 2.A BSI-18 instrument

 Appendix 2.B Item reliability

 Appendix 2.C Cronbach's alpha coefficient

 Appendix 2.D Calculating probabilities using PROBIT regression Coefficients

3 Structuralequations withlatent variables

 3.1 MIMIC model

 3.2 Structuralequationmodel

 3.3 Correcting for measurement errorsin single indicator variables

 3.4 Testinginteractionsinvolvinglatentvariables

 Appendix 3.A Influence of measurement errors

4 Latent growth models for longitudinal data analysis

 4.1 LinearLGM

 4.2 NonlinearLGM

 4.3 Multi-processLGM

 4.4 Two-partLGM

 4.5 LGM with categoricaloutcomes

5 Multi-groupmodeling

 5.1 Multi-group CFA model

5.1.1 Multi-group first-order CFA

5.1.2 Multi-group second-order CFA

 5.2 Multi-group SEM model

 5.3 Multi-groupLGM

6 Mixturemodeling

 6.1 LCAmodel

6.1.1 ExampleofLCA

6.1.2 Example of LCA model with covariates

 6.2 LTAmodel

6.2.1 ExampleofLTA

 6.3 Growth mixture model

6.3.1 Example of GMM

 6.4 Factor mixture model

 Appendix 6.A Including covariate in the LTA model

7 Sample size for structural equation modeling

 7.1 The rules of thumb for sample size needed for SEM

 7.2 Satorra and Saris's method for sample size estimation

7.2.1 Application of Satorra and Saris's method to CFA model

7.2.2 Application of Satorra and Saris's method to LGM

 7.3 Monte Carlo simulation for sample size estimation

7.3.1 Application ofMonte Carlo simulation to CFA model

7.3.2 Application of Monte Carlo simulation to LGM

7.3.3 Application of Monte Carlo simulation to LGM with covariate

7.3.4 Application of Monte Carlo simulation to LGM with missing values

 7.4 Estimate sample size for SEM based on model fit indices

7.4.1 Application of MacCallum, Browne and Sugawara's method

7.4.2 Application of Kim's method

References

Index