Handbook of Structural Equation Modeling

This book has been replaced by Handbook of Structural Equation Modeling, Second Edition, ISBN 978-1-4625-4464-6.

Table of Contents:
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

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.

Review :
"The impressive author list reads like a 'who's who' in SEM--and they cover all the major (and most of the minor) issues in the field. I highly recommend this handbook to researchers using SEM as the next book they need after reading one of the introductory texts. I like the hands-on approach in many of the chapters; in my view, there is no such thing as a superfluous example."--Joop J. Hox, PhD, Department of Methodology and Statistics, Utrecht University, The Netherlands "An exceptionally useful and comprehensive resource, the Handbook of Structural Equation Modeling covers all facets of this flexible approach to data analysis. Accessible to analysts in numerous scientific fields, it begins with foundational materials on model formulation and representation, continuing through estimation, assessment, and interpretation. With due attention to conceptual issues and matters of implementation alike, this handbook strikes an excellent balance between fundamentals, extensions, applications, and current innovations."--Peter V. Marsden, PhD, Edith and Benjamin Geisinger Professor of Sociology, Harvard College Professor, and Dean of Social Science, Harvard University "An absolutely remarkable book. It spans every conceivable topic--from history to fundamentals to the most advanced topics under development--and brings together the leading scientists currently working in statistical design and analysis. The chapters are technically rigorous yet accessible to a broad audience. This is a great reference for all who are interested in SEM, whether novices or seasoned professionals."--Patrick J. Curran, PhD, Department of Psychology, University of North Carolina at Chapel Hill "This cutting-edge book is an exceptionally ambitious scholarly masterpiece. Broad in scope, the many self-contained chapters cover the basics and extend SEM to the four corners of statistical analysis. This is a crucial contribution to the literature for behavioral and social scientists, applied statisticians and econometricians, and researchers in the business, education, and health disciplines."--Richard P. Bagozzi, PhD, Ross School of Business, University of Michigan "Every theoretician, methodologist, and user of SEM will find expert overviews of topics relevant to their work in this essential volume on all facets of SEM. Selected chapters can provide great ancillary reading in an introductory SEM course. My congratulations to Hoyle and his superb contributors!"--Peter M. Bentler, PhD, Departments of Psychology and Statistics, University of California, Los Angeles "The Handbook includes 40 chapters covering the full range of current topics, by an outstanding set of authors. It serves as an excellent textbook for classes in SEM that go beyond the basics and cover more advanced topics, or as a sourcebook for applied researchers."--Stephen G. West, PhD, Department of Psychology, Arizona State University