Description
"Wise social scientists will make a beeline for the 2nd edition of David Kaplan's classic text. An essential read for students and professionals who want to move beyond cookbook presentations with checklists towards deeper understanding and thoughtful application."
—Judith D. Singer, Harvard University
Using detailed, empirical examples, Structural Equation Modeling, Second Edition, presents a thorough and sophisticated treatment of the foundations of structural equation modeling (SEM). It also demonstrates how SEM can provide a unique lens on the problems social and behavioral scientists face.
Thoroughly revised to address recent developments, this new edition includes:
- The foundations of SEM, including path analysis and factor analysis.
- Traditional SEM for continuous latent variables, including latent growth curve modeling for continuous growth factors, and issues in testing assumptions of SEM.
- SEM for categorical latent variables, including latent class analysis, Markov models (latent and mixed latent), and growth mixture modeling.
- Philosophical issues in the practice of SEM, including the problem of causal inference.
Intended Audience
While the book assumes some knowledge and background in statistics, it guides readers through the foundations and critical assumptions of SEM in an easy-to-understand manner.
Table of Contents
Preface to the Second Edition
1. Historical Foundations of Structural Equation Modeling for Continuous and Categorical Latent Variables
2. Path Analysis: Modeling Systems of Structural Equations Among Observed Variables
3. Factor Analysis
4. Structural Equation Models in Single and Multiple Groups
5. Statistical Assumptions Underlying Structural Equation Modeling
6. Evaluating and Modifying Structural Equation Models
7. Multilevel Structural Equation Modeling
8. Latent Growth Curve Modeling
9. Structural Models for Categorical and Continuous Latent Variables
10. Epilogue: Toward a New Approach to the Practice of Structural Equation Modeling
