Main description:

Longitudinal Structural Equation Modeling is a comprehensive resource that reviews structural equation modeling (SEM) strategies for longitudinal data to help readers determine which modeling options are available for which hypotheses.

This accessibly written book explores a range of models, from basic to sophisticated, including the statistical and conceptual underpinnings that are the building blocks of the analyses. By exploring connections between models, it demonstrates how SEM is related to other longitudinal data techniques and shows when to choose one analysis over another. Newsom emphasizes concepts and practical guidance for applied research rather than focusing on mathematical proofs, and new terms are highlighted and defined in the glossary. Figures are included for every model along with detailed discussions of model specification and implementation issues and each chapter also includes examples of each model type, descriptions of model extensions, comment sections that provide practical guidance, and recommended readings.

Expanded with new and updated material, this edition includes many recent developments, a new chapter on growth mixture modeling, and new examples. Ideal for graduate courses on longitudinal (data) analysis, advanced SEM, longitudinal SEM, and/or advanced data (quantitative) analysis taught in the behavioral, social, and health sciences, this new edition will continue to appeal to researchers in these fields.

Contents:

Contents

List of Figures

List of Tables

Preface to the Second Editon

Preface to the First Edition

Acknowledgements

Example Data Sets

Chapter 1. Review of Some Key Latent Variable Principles

Chapter 2. Longitudinal Measurement Invariance

Chapter 3. Structural Models for Comparing Dependent Means and Proportions

Chapter 4. Fundamental Concepts of Stability and Change

Chapter 4. Claps and Claptraps: How Political Speakers and Audiences Interact

Chapter 5. Cross-Lagged Panel Models

Chapter 6. Latent State-Trait Models

Chapter 7. Linear Latent Growth Curve Models

Chapter 8. Nonlinear Latent Growth Curve Models

Chapter 9. Nonlinear Latent Growth Curve Models

Chapter 10. Latent Class and Latent Transition

Chapter 11. Growth Mixture Models

Chapter 12. Intensive Longitudinal Models: Time Series and Dynamic Structural Equation Models

Chapter 13. Survival Analysis Models

Chapter 14. Missing Data and Attrition

Appendix A: Notation

Appendix B: Why Does the Single Occasion Scaling Constraint Approach Work?

Appendix C: A Primer on the Calculus of Change

Glossary

Index