Handbook of Structural Equation Modeling

The first comprehensive structural equation modeling (SEM) handbook, this accessible volume offers broad and deep coverage of both the mechanics of SEM and specific SEM strategies and applications. The editor, contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM, such as intensive longitudinal assessments, dyadic data, brain imaging, and genotyping. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results.

Rick H. Hoyle is Professor of Psychology and Neuroscience at Duke University, where he serves as Associate Director of the Center for Child and Family Policy and Director of the Methodology and Statistics Core in the Transdisciplinary Prevention Research Center. He is a Fellow of the Association for Psychological Science, the American Psychological Association, and the Society of Experimental Social Psychology. He has written extensively on SEM and other statistical and methodological strategies for the study of complex social and behavioral processes.

I. Background
1. Introduction and Overview, Rick H. Hoyle
2. Key Advances in the History of Structural Equation Modeling, Ross L. Matsueda
3. Graphical Representation of Structural Equation Models Using Path Diagrams, Moon-ho Ringo Ho, Stephen Stark, and Olexander Chernyshenko
4. Latent Variables in Structural Equation Modeling, Kenneth A. Bollen and Rick H. Hoyle
5. The Causal Foundations of Structural Equation Modeling, Judea Pearl
6. Simulation Methods in Structural Equation Modeling, Deborah L. Bandalos and Phillip Gagné
II. Fundamentals
7. Assumptions in Structural Equation Modeling, Rex B. Kline
8. Model Specification in Structural Equation Modeling, Rick H. Hoyle
9. Identification: A Nontechnical Discussion of a Technical Issue, David A. Kenny and Stephanie Milan
10. Estimation in Structural Equation Modeling, Pui-Wa Lei and Qiong Wu
11. Power Analysis for Tests of Structural Equation Models, Taehun Lee, Li Cai, and Robert C. MacCallum
12. Categorical Data in the Structural Equation Modeling Framework, Michael C. Edwards, R. J. Wirth, Carrie R. Houts, and Nuo Xi
13. Model Fit and Model Selection in Structural Equation Modeling, Stephen G. West, Aaron B. Taylor, and Wei Wu
14. Model Modification in Structural Equation Modeling, Chih-Ping Chou and Jimi Huh
15. Equivalent Models: Concepts, Problems, Alternatives, Larry J. Williams
III. Implementation
16. Preparing Data for Structural Equation Modeling: Doing Your Homework, Patrick S. Malone and Jill B. Lubansky
17. Structural Equation Modeling with Missing Data, John W. Graham and Donna L. Coffman
18. Bootstrapping Standard Errors and Data-Model Fit Statistics in Structural Equation Modeling, Gregory R. Hancock and Min Liu
19. Choosing Structural Equation Modeling Computer Software: Snapshots of LISREL, EQS, Amos, and Mplus, Barbara M. Byrne
20. Structural Equation Modeling in R with the sem and OpenMx Packages, John Fox, Jarrett E. Byrnes, Steven Boker, and Michael C. Neale
21. The Structural Equation Modeling Research Report, Anne Boomsma, Rick H. Hoyle, and A. T. Panter
IV. Basic Applications
22. Confirmatory Factor Analysis, Timothy A. Brown and Michael T. Moore
23. Investigating Measurement Invariance Using Confirmatory Factor Analysis, Roger E. Millsap and Margarita Olivera-Aguilar
24. A Flexible Structural Equation Modeling Approach for Analyzing Means, Samuel B. Green and Marilyn S. Thompson
25. Mediation/Indirect Effects in Structural Equation Modeling, JeeWon Cheong and David P. MacKinnon
26. Structural Equation Models of Latent Interaction, Herbert W. Marsh, Zhonglin Wen, Benjamin Nagengast, and Kit-Tai Hau
27. Autoregressive Longitudinal Models, Jeremy C. Biesanz
28. Scale Construction and Development Using Structural Equation Modeling, Tenko Raykov
V. Advanced Applications
29. Measurement Models for Ordered-Categorical Indicators, James A. Bovaird and Natalie A. Koziol
30. Multilevel Structural Equation Modeling, Sophia Rabe-Hesketh, Anders Skrondal, and Xiaohui Zheng
31. An Overview of Growth Mixture Modeling: A Simple Nonlinear Application in OpenMx, Mariya P. Shiyko, Nilam Ram, and Kevin J. Grimm
32. Latent Curve Modeling of Longitudinal Growth Data, John J. McArdle
33. Dynamic Factor Models for Longitudinally Intensive Data: Description and Estimation via Parallel Factor Models of Cholesky Decomposition, Phillip Wood
34. Latent Trait-State Models, David A. Cole
35. Longitudinal Structural Models for Assessing Dynamics in Dyadic Interactions, Emilio Ferrer and Hairong Song
36. Structural Equation Modeling in Genetics, Sanja Franic, Conor V. Dolan, Denny Borsboom, and Dorret I. Boomsma
37. Structural Equation Models of Imaging Data, Anthony R. McIntosh and Andrea B. Protzner
38. Bayesian Structural Equation Modeling, David Kaplan and Sarah Depaoli
39. Spatial Structural Equation Modeling, Melanie M. Wall
40. Automated Structural Equation Modeling Strategies, George A. Marcoulides and Marsha Ing