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.
|Publisher:||Guilford Publications, Inc.|
|Product dimensions:||9.20(w) x 7.40(h) x 1.40(d)|
About the Author
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.
Table of Contents
I. Background1. Introduction and Overview, Rick H. Hoyle2. Key Advances in the History of Structural Equation Modeling, Ross L. Matsueda3. Graphical Representation of Structural Equation Models Using Path Diagrams, Moon-ho Ringo Ho, Stephen Stark, and Olexander Chernyshenko4. Latent Variables in Structural Equation Modeling, Kenneth A. Bollen and Rick H. Hoyle5. The Causal Foundations of Structural Equation Modeling, Judea Pearl6. Simulation Methods in Structural Equation Modeling, Deborah L. Bandalos and Phillip GagnéII. Fundamentals7. Assumptions in Structural Equation Modeling, Rex B. Kline8. Model Specification in Structural Equation Modeling, Rick H. Hoyle9. Identification: A Nontechnical Discussion of a Technical Issue, David A. Kenny and Stephanie Milan10. Estimation in Structural Equation Modeling, Pui-Wa Lei and Qiong Wu11. Power Analysis for Tests of Structural Equation Models, Taehun Lee, Li Cai, and Robert C. MacCallum12. Categorical Data in the Structural Equation Modeling Framework, Michael C. Edwards, R. J. Wirth, Carrie R. Houts, and Nuo Xi13. Model Fit and Model Selection in Structural Equation Modeling, Stephen G. West, Aaron B. Taylor, and Wei Wu14. Model Modification in Structural Equation Modeling, Chih-Ping Chou and Jimi Huh15. Equivalent Models: Concepts, Problems, Alternatives, Larry J. WilliamsIII. Implementation16. Preparing Data for Structural Equation Modeling: Doing Your Homework, Patrick S. Malone and Jill B. Lubansky17. Structural Equation Modeling with Missing Data, John W. Graham and Donna L. Coffman18. Bootstrapping Standard Errors and Data–Model Fit Statistics in Structural Equation Modeling, Gregory R. Hancock and Min Liu19. Choosing Structural Equation Modeling Computer Software: Snapshots of LISREL, EQS, Amos, and Mplus, Barbara M. Byrne20. Structural Equation Modeling in R with the sem and OpenMx Packages, John Fox, Jarrett E. Byrnes, Steven Boker, and Michael C. Neale21. The Structural Equation Modeling Research Report, Anne Boomsma, Rick H. Hoyle, and A. T. PanterIV. Basic Applications22. Confirmatory Factor Analysis, Timothy A. Brown and Michael T. Moore23. Investigating Measurement Invariance Using Confirmatory Factor Analysis, Roger E. Millsap and Margarita Olivera-Aguilar24. A Flexible Structural Equation Modeling Approach for Analyzing Means, Samuel B. Green and Marilyn S. Thompson25. Mediation/Indirect Effects in Structural Equation Modeling, JeeWon Cheong and David P. MacKinnon26. Structural Equation Models of Latent Interaction, Herbert W. Marsh, Zhonglin Wen, Benjamin Nagengast, and Kit-Tai Hau27. Autoregressive Longitudinal Models, Jeremy C. Biesanz28. Scale Construction and Development Using Structural Equation Modeling, Tenko RaykovV. Advanced Applications29. Measurement Models for Ordered-Categorical Indicators, James A. Bovaird and Natalie A. Koziol30. Multilevel Structural Equation Modeling, Sophia Rabe-Hesketh, Anders Skrondal, and Xiaohui Zheng31. An Overview of Growth Mixture Modeling: A Simple Nonlinear Application in OpenMx, Mariya P. Shiyko, Nilam Ram, and Kevin J. Grimm32. Latent Curve Modeling of Longitudinal Growth Data, John J. McArdle33. Dynamic Factor Models for Longitudinally Intensive Data: Description and Estimation via Parallel Factor Models of Cholesky Decomposition, Phillip Wood34. Latent Trait–State Models, David A. Cole35. Longitudinal Structural Models for Assessing Dynamics in Dyadic Interactions, Emilio Ferrer and Hairong Song36. Structural Equation Modeling in Genetics, Sanja Franic, Conor V. Dolan, Denny Borsboom, and Dorret I. Boomsma37. Structural Equation Models of Imaging Data, Anthony R. McIntosh and Andrea B. Protzner38. Bayesian Structural Equation Modeling, David Kaplan and Sarah Depaoli39. Spatial Structural Equation Modeling, Melanie M. Wall40. Automated Structural Equation Modeling Strategies, George A. Marcoulides and Marsha Ing
Graduate students, instructors, researchers, and evaluators in psychology, education, human development and family studies, management, sociology, social work, nursing, public health, criminal justice, and communication. Serves as a text for graduate-level courses in structural equation modeling, multivariate statistics, latent variable modeling, advanced quantitative methods, or the dissertation.