Longitudinal Structural Equation Modeling

Second Edition

Todd D. Little
Foreword by Noel A. Card

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January 2, 2024
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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 sample

- 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 Repeated-Measures 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 Multiple-Group 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 Cross-Lagged Panel Model

- Illustrative Examples of the RI-CLPM

- Extensions to the RI-CLPM

- 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

- Pseudo-MTMM Models

- Bifactor and Higher-Order 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