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by Xiaoqian Wang, Jichuan Wang
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
6.2 LTA model
6.3 Growth Mixture Model
6.4 Factor Mixture Model
Appendix 6.A Including covariate in the LTA model
Chapter 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
7.3 Monte Carlo Simulation for Sample Size Estimation
7.4 Estimate Sample Size for SEM Based on Model Fit Indices
References
Index
Wiley Series in Probability and Statistics
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