- 502 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
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.
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Information
Table of contents
- Cover Page
- Half-Title Page
- Series Page
- Title Page
- Copyright Page
- Dedication Page
- Contents
- List of Figures
- List of Tables
- Preface to the Second Edition
- Preface to the First Edition
- Acknowledgments
- Example Data Sets
- 1 Review of Some Key Latent Variable Principles
- 2 Longitudinal Measurement Invariance
- 3 Structural Models for Comparing Dependent Means and Proportions
- 4 Fundamental Concepts of Stability and Change
- 5 Cross-Lagged Panel Models
- 6 Latent State-Trait Models
- 7 Linear Latent Growth Curve Models
- 8 Nonlinear Latent Growth Curve Models
- 9 Latent Change Score Models
- 10 Latent Class and Latent Transition Models
- 11 Growth Mixture Models
- 12 Intensive Longitudinal Models: Time Series and Dynamic Structural Equation Models
- 13 Survival Analysis Models
- 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