Longitudinal Structural Equation Modeling with Mplus: A Latent State-Trait Perspective (Paperback)

List of AbbreviationsGuide to Statistical Symbols1. A Measurement Theoretical Framework for Longitudinal Data: Introduction to Latent State?Trait Theory1.1 Introduction1.2 Latent State?Trait Theory1.3 Chapter Summary1.4 Recommended Readings2. Single-Factor Longitudinal Models for Single-Indicator Data2.1 Introduction2.2 The Random Intercept Model2.3 The Random and Fixed Intercepts Model2.4 The ξ-Congeneric Model2.5 Chapter Summary2.6 Recommended Reading3. Multifactor Longitudinal Models for Single-Indicator Data3.1 Introduction3.2 The Simplex Model3.3 The Latent Change Score Model3.4 The Trait?State?Error Model3.5 Latent Growth Curve Models3.6 Chapter Summary3.7 Recommended Readings4. Testing Measurement Equivalence in Longitudinal Studies4.1 Introduction4.2 The Latent State (LS) Model4.3 The Latent State Model with Indicator-Specific Residual Factors (LS-IS Model)4.4 Chapter Summary4.5 Recommended Readings5. Multiple-Indicator Longitudinal Models5.1 Introduction5.2 Latent State Change (LSC) Models5.3 The Latent Autoregressive/Cross-Lagged States (LACS) Model5.4 Latent State?Trait (LST) Models5.5 Latent Trait Change (LTC) Models5.6 Chapter Summary5.7 Recommended Readings6. Modeling Intensive Longitudinal Data6.1 Introduction6.2 Special features of Intensive Longitudinal Data6.3 Specifying Longitudinal SEMs for Intensive Longitudinal Data6.4 Chapter Summary6.5 Recommended Readings7. Missing Data Handling7.1 Introduction7.2 Missing Data Mechanisms7.3 Maximum Likelihood Missing Data Handling7.4 Multiple Imputation (MI)7.5 Planned Missing Data Designs7.6 Chapter Summary7.7 Recommended Readings8. How to Choose between Models and Report the Results8.1 Model Selection8.2 Reporting Results8.3 Chapter Summary8.4 Recommended ReadingsReferencesAuthor IndexSubject Index