Foreword, Noel A. Card 1. Overview and Foundations of Structural Equation Modeling - An Overview of the Conceptual Foundations of SEM - Sources of Variance in Measurement - Characteristics of Indicators and Constructs - A Simple Taxonomy of Indicators and Their Roles - Rescaling Variables - Parceling - What Changes and How? - Some Advice for SEM Programming - Philosophical Issues and How I Approach Research - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 2. Design Issues in Longitudinal Studies - Timing of Measurements and Conceptualizing Time - Modeling Developmental Processes in Context - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 3. Modern Approaches to Missing Data in Longitudinal Studies - Planning for Missing Data - Planned Missing Data Designs in Longitudinal Research - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 4. The Measurement Model - Drawing and Labeling Conventions - Defining the Parameters of a Construct - Scale Setting - Identification - Adding Means to the Model: Scale Setting and Identification with Means - Adding a Longitudinal Component to the CFA Model - Adding Phantom/Rescaling Constructs to the CFA Model - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 5. Model Fit, Sample Size, and Power - Model Fit and Types of Fit Indices - Sample Size - Power - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 6. The Longitudinal CFA Model - Factorial Invariance - A Small (Nearly Perfect) Data Example - A Larger Example Followed by Tests of the Latent Construct Relations - An Application of a Longitudinal SEM to a RepeatedMeasures Experiment - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 7.
Specifying and Interpreting a Longitudinal Panel Model - Basics of a Panel Model - The Basic Simplex Change Process - Building a Panel Model - Illustrative Examples of Panel Models - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 8. Multiple-Group Longitudinal Models - A Multiple-Group SEM - A Multiple-Group Longitudinal Model for Conducting an Intervention Evaluation - A Dynamic P-Technique MultipleGroup Longitudinal Model - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 9. The Random Intercept Cross-Lagged Panel Model, Danny Osborne and Todd D. Little - Problems with Traditional Cross-Lagged Panel Models - The Random Intercept CrossLagged Panel Model - Illustrative Examples of the RICLPM - Extensions to the RICLPM - Final Considerations - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 10. Mediation and Moderation - Making the Distinction between Mediators and Moderators - Moderation - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 11. Multilevel Growth Curves and Multilevel SEM - Longitudinal Growth Curve Model - Multivariate Growth Curve Models - Multilevel Longitudinal Model - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 12. Longitudinal Mixture Modeling: Finding Unknown Groups, E. Whitney G.
Moore and Todd D. Little - General Background - Analysis Types - Finite Mixture Modeling Overview - Latent Class Analysis - Latent Profile Analysis - Latent Transition Analysis - Other LTA Modeling Approaches - Developments and Extensions into the Future of Finite Mixture Modeling - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 13. Bayesian Longitudinal Structural Equation Modeling, Mauricio Garnier-Villarreal and Todd D. Little - The Bayesian Perspective - Bayesian Inference - Advantages of a Bayesian Framework - MCMC Estimation - Bayesian Structural Equation Modeling - Information Criteria - Bayes Factor - Applied Example - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 14. Jambalaya: Complex Construct Representations and Decompositions - Multitrait-Multimethod Models - PseudoMTMM Models - Bifactor and HigherOrder Factor Models - Contrasting Different Variance Decompositions - Digestif - Key Terms and Concepts Introduced in This Chapter - Recommended Readings References Author Index Subject Index About the Author.