Structural equation modeling with Mplus : methods and applications - Wang, Jichuan (Wiley/Higher Education Press, 2012)
Machine generated contents note: Preface 1. Introduction 1.1 Model formulation 1.2 Model identification 1.3 Model estimation 1.4 Model evaluation 1.5 Model modification 2. Confirmatory factor analysis (CFA) Models 2.1 Basics of CFA 2.2 CFA with continuous indicators 2.3 CFA with non-normal and censored continuous indicators 2.4 CFA with categorical indicators 2.5 Higher-order CFA 3. Structural Equation Models (SEM) 3.1 Multiple indicators and multiple causes (MIMIC) Model 3.2 Structural equation model 3.3 Correcting for measurement errors in single indicator variables 3.4 Testing interactions involving latent variables 4. Latent growth modeling (LGM) for longitudinal data 4.1 Linear latent growth model (LGM) 4.2 Non-linear LGM 4.3 LGM with multiple growth processes 4.4 Two-part LGM 4.5 LGM with categorical outcomes 5. Multi-Group Modeling 5.1 Multi-group confirmatory factor analysis (CFA) model 5.2 Multi-group structural equation model (SEM) 5.3 Multi-group latent growth model (LGM) 6. Mixture models 6.1 Latent class analysis (LCA) model 6.2 Latent transition analysis (LTA) model 6.3 Growth mixture model (GMM) 6.4 Factor Mixture Model (FMM) 7. Sample Size for Structural Equation Modeling 7.1 Rule of thumbs for sample size needed for SEM 7.2 Satorra-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 statistics/indexes References Index.