Synopses & Reviews
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
Review
"In this welcome addition to the personal libraries of quantitative and applied researchers alike, Rick Hoyle brings considerable editorial skill to bear on the 40-chapter Handbook of Structural Equation Modeling. The breadth of topics covered in this reference text leaves little doubt that a firm grasp of data analysis with latent variables is essential to the advancement of scholarship in the social and behavioral sciences....An excellent resource for the many conceptual and analytical problems frequently encountered by researchers making use of SEM. About half of the chapters remain true to the Handbook's objective of accessibility to a novice readership (Chapters 1-4, 6-10, 12, 13, 15, 16, 19, 21, 22, 27-29, and 31), and several others are more suitable for readers with a modest background in latent variable modeling (Chapters 11, 14, 18, 20, 23, 25, 32, 34, and 36-40). A small number of chapters are suitable for advanced readers and those actively working in a quantitative discipline (Chapters 5, 30, 33, and 35). As a reference text, it is a strength of the Handbook that chapters vary in extent to which readers are presumed to possess statistical sophistication. As novice readers develop a deeper understanding of SEM fundamentals, the Handbook will continue to serve as a valuable reference for advanced applications....It is an impressive achievement that across 40 self-contained chapters and more than 75 authors there appeared very little unnecessary content overlap and no contradictory recommendations. Readers will find themselves returning to the Handbook again and again as a starting point for their work on virtually any topic currently within the broad reach of SEM."--Structural Equation Modeling
Synopsis
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.
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. 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 DataModel 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 TraitState 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