Structural Equation Modeling: Applications Using Mplus

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Wiley Series in Probability and Statistics

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Preface

Chapter 1: Introduction

1.1 Model Formulation

1.2 Model Identification

1.3 Model Estimation

1.4 Model Evaluation

1.5 Model Modification

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

Chapter 2: Confirmatory Factor Analysis

2.1 Basics of CFA Model

2.2 CFA Model with Continuous Indicators

2.3 CFA Model with Non-Normal and Censored Continuous Indicators

2.4 CFA Model with Categorical Indicators

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

Chapter 3: Structural Equations with Latent Variables

3.1 MIMIC Model

3.2 Structural Equation Model

3.3 Correcting for Measurement Errors in Single Indicator Variables

3.4 Testing Interactions Involving Latent Variables

Appendix 3.A Influence of Measurement Errors

Chapter 4: Latent Growth Models for Longitudinal Data Analysis

4.1 Linear LGM

4.2 Nonlinear LGM

4.3 Multi-Process LGM

4.4 Two-Part LGM

4.5 LGM with Categorical Outcomes

Chapter 5: Multi-Group Modeling

5.1 Multi-Group CFA Model

5.2 Multi-Group SEM model

5.3 Multi-Group LGM

Chapter 6: Mixture Modeling

6.1 LCA Model ...