Handbook of Structural Equation Modeling

Edited by Rick H. Hoyle

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October 1, 2014
ISBN 9781462516797
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740 Pages
Size: 7⅜" x 9¼"
Copyright Date: 2012
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May 21, 2012
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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.

“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 . 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


“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

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 Editor

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.

Contributors

Deborah L. Bandalos, Center for Assessment and Research Studies, James Madison University, Harrisonburg, Virginia

Jeremy C. Biesanz, Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada

Steven Boker, Department of Psychology, University of Virginia, Charlottesville, Virginia

Kenneth A. Bollen, Department of Sociology, University of North Carolina, Chapel Hill, North Carolina

Anne Boomsma, Department of Sociology, University of Groningen, Groningen, The Netherlands

Dorret I. Boomsma, Department of Biological Psychology, Free University, Amsterdam, The Netherlands

Denny Borsboom, Department of Psychology, Faculty of Social and Behavioral Sciences, University of Amsterdam, Amsterdam, The Netherlands

James A. Bovaird, QQPM Program, Department of Educational Psychology, University of Nebraska, Lincoln, Nebraska

Timothy A. Brown, Center for Anxiety and Related Disorders, Department of Psychology, Boston University, Boston, Massachusetts

Barbara M. Byrne, School of Psychology, University of Ottawa, Ottawa, Ontario, Canada

Jarrett E. Byrnes, Santa Barbara Coastal LTER, Marine Science Institute, University of California, Santa Barbara, California

Li Cai, Department of Education, Graduate School of Education and Information Studies, University of California, Lost Angeles, California

JeeWon Cheong, Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania

Olexander Chernyshenko, Division of Strategy, Management, and Organization, School of Business, Nanyang Technical University

Chih-Ping Chou, Institute of Health Promotion and Disease Prevention Research, University of Southern California, Los Angeles, California

Donna L. Coffman, The Methodology Center, Pennsylvania State University, State College, Pennsylvania

David A. Cole, Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee

Sarah Depaoli, School of Social Sciences, Humanities, and Arts, University of California, Merced, California

Conor V. Dolan, Department of Psychology, Faculty of Social and Behavioral Sciences, University of Amsterdam, Amsterdam, The Netherlands

Michael C. Edwards, Department of Psychology, Ohio State University, Columbus, Ohio

Emilio Ferrer, Department of Psychology, University of California, Davis, California

John Fox, Department of Sociology, McMaster University, Hamilton, Ontario, Canada

Sanja Franic, Department of Biological Psychology, Faculty of Psychology and Education, VU University, Amsterdam, The Netherlands

Phillip Gagné, Department of Educational Policy Studies, Georgia State University, Atlanta, Georgia

John W. Graham, Department of Biobehavioral Health, Pennsylvania State University, University Park, Pennsylvania

Samuel B. Green, School of Social and Family Dynamics, Arizona State University, Tempe, Arizona

Kevin J. Grimm, Department of Psychology, University of California, Davis, California

Gregory R. Hancock, Department of Measurement, Statistics, and Evaluation, University of Maryland, College Park, Maryland

Kit-Tai Hau, Faculty of Education, The Chinese University of Hong Kong, Shatin, Hong Kong

Moon-ho Ringo Ho, Division of Psychology, School of Humanities and Social Sciences, Nanyang Technological University, Singapore

Carrie R. Houts, Department of Psychology, Ohio State University, Columbus, Ohio

Rick H. Hoyle, Department of Psychology and Neuroscience, Duke University, Durham, North Carolina

Jimi Huh, Department of Preventative Medicine, Keck School of Medicine, University of Southern California, Alhambra, California

Marsha Ing, Graduate School of Education, University of California, Riverside, California

David Kaplan, Department of Educational Psychology, University of Wisconsin, Madison, Wisconsin

David A. Kenny, Department of Psychology, University of Connecticut, Storrs, Connecticut

Rex B. Kline, Department of Psychology, Concordia University, Montreal, Quebec, Canada

Natalie A. Koziol, Department of Educational Psychology, University of Nebraska, Lincoln, Nebraska

Taehun Lee, Department of Education, Graduate School of Education and Information Studies, University of California, Los Angeles, California

Pui-Wa Lei, Department of Educational/School Psychology and Special Education, Pennsylvania State University, University Park, Pennsylvania

Min Liu, Department of Educational Psychology, University of Hawaii, Manoa, Hawaii

Jill B. Lubansky, Department of Psychology, University of South Carolina, Columbia, South Carolina

Robert C. MacCallum, Department of Psychology, University of North Carolina, Chapel Hill, North Carolina

David P. MacKinnon, Department of Psychology, Arizona State University, Tempe, Arizona

Patrick S. Malone, Department of Psychology, University of South Carolina, Columbia, South Carolina

George A. Marcoulides, Graduate School of Education, University of California, Riverside, California

Herbert W. Marsh, Department of Education, University of Oxford, Oxford, United Kingdom

Ross L. Matsueda, Department of Sociology, University of Washington, Seattle, Washington

John J. McArdle, Department of Psychology, University of Southern California, Los Angeles, California

Anthony R. McIntosh, The Rotman Research Institute, Baycrest Centre for Geriatric Care, Toronto, Ontario, Canada

Stephanie Milan, Department of Psychology, University of Connecticut, Storrs, Connecticut

Roger E. Millsap, Department of Psychology, Arizona State University, Tempe, Arizona

Michael T. Moore, Center for Anxiety and Related Disorders, Department of Psychology, Boston University, Boston, Massachusetts

Benjamin Nagengast, Center for Educational Science and Psychology, Department of Education, University of Tübingen, Tübingen, Germany

Michael C. Neale, Department of Psychiatry and Human Genetics, Virginia Commonwealth University, Richmond, Virginia

Margarita Olivera-Aguilar, Department of Psychology, Arizona State University, Tempe, Arizona

Abigail T. Panter, Department of Psychology, University of North Carolina, Chapel Hill, North Carolina

Judea Pearl, Department of Computer Science, University of California, Los Angeles, California

Andrea B. Protzner, Department of Neuropsychology, Toronto Western Hospital and Research Center, Toronto, Ontario, Canada

Sophia Rabe-Hesketh, Graduate School of Education, University of California, Berkeley, California

Nilam Ram, Department of Human Development and Family Studies, Pennsylvania State University, University Park, Pennsylvania

Tenko Raykov, Department of Measurement and Qualitative Methods, Michigan State University, East Lansing, Michigan

Mariya P. Shiyko, Department of Counseling and Applied Psychology, Bouve College of Health Sciences, Northeastern University, Boston, Massachusetts

Anders Skrondal, Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway

Hairong Song, Department of Psychology, University of Oklahoma, Norman, Oklahoma

Stephen Stark, Department of Psychology, University of South Florida, Tampa, Florida

Aaron B. Taylor, Department of Psychology, Texas A and M University, College Station, Texas

Marilyn S. Thompson, School of Social and Family Dynamics, Arizona State University, Tempe, Arizona

Melanie M. Wall, Division of Biostatistics, New York State Psychiatric Institute, and Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York

Zhonglin Wen, Department of Psychology, South China Normal University, Guangzhou, China

Stephen G. West, Department of Psychology, Arizona State University, Tempe Arizona

Larry J. Williams, Center for the Advancement of Research Methods and Analysis, School of Business Administration, Wayne State University, Detroit, Michigan

R. J. Wirth, Vector Psychometric Group, LLC, Durham, North Carolina

Phillip Wood, Department of Psychological Sciences, University of Missouri, Columbia, Missouri

Qiong Wu, Center for Population and Development Studies, Harvard University, Cambridge, Massachusetts

Wei Wu, Department of Psychology, University of Kansas, Lawrence, Kansas

Nuo Xi, Educational Testing Service, Princeton, New Jersey

Xiaohui Zheng, Institutional Research, University of California, Berkeley, California

Audience

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.

Course Use

Serves as a text for graduate-level courses in structural equation modeling, multivariate statistics, latent variable modeling, advanced quantitative methods, or the dissertation.