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Handbook of Structural Equation Modeling

Second Edition

Edited by Rick H. Hoyle

Hardcovere-bookprint + e-book
Hardcover
February 17, 2023
ISBN 9781462544646
Price: $125.00
785 Pages
Size: 7⅜" x 9¼"
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November 10, 2022
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785 Pages
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The definitive one-stop resource on structural equation modeling (SEM) from leading methodologists is now in a significantly revised second edition. Twenty-three new chapters cover model selection, bifactor models, item parceling, multitrait–multimethod models, exploratory SEM, mixture models, SEM with small samples, and more. The book moves from fundamental SEM topics (causality, visualization, assumptions, estimation, model fit, and managing missing data); to major model types focused on unobserved causes of covariance between observed variables; to more complex, specialized applications. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with the reader's data. The expanded companion website presents full data sets, code, and output for many of the chapters, as well as bonus selected chapters from the prior edition.

New to This Edition

“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….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 (on the first edition)


“A tremendous resource that will be of great value to researchers and students who study SEM and apply it in their research. SEM statistical models and methods have continued to be developed over the past decade; the second edition of this volume incorporates these developments along with coverage of new estimation algorithms, computer programs, and empirical applications. The Handbookcould be used in graduate courses—students could be introduced to its basic chapters for contemporary articulations of SEM models and methods, and to subsequent chapters for specialized models and applications.”

—Kenneth C. Land, PhD, John Franklin Crowell Professor Emeritus of Sociology and Demography and Research Professor, Social Science Research Institute, Duke University


“The world of SEM—in terms of both applications and methodological advances—has expanded dramatically in recent years. Just learning SEM in the first place can be hard enough, let alone keeping up with all of the new developments. The second edition of this handbook is an invaluable resource for anyone seeking a comprehensive summary of current possibilities and best practices using SEM. The authors are all leading experts who have tackled an exhaustive list of topics, with an eye toward accessibility for new learners, but without shying away from the material’s inherent complexity.”

—Lesa Hoffman, PhD, Department of Psychological and Quantitative Foundations, College of Education, University of Iowa


“SEM provides unity to disparate analyses and facilitates model evaluation and modification in the everyday practice of uncountable researchers. This is an impressive, 'must-read' book for practitioners of SEM and those who want to apprehend the strength and usefulness of the SEM idea.”

—Albert Satorra, PhD, Emeritus Professor of Statistics, Universitat Pompeu Fabra, Barcelona, Spain


“This handbook is an outstanding desk reference on a wide variety of advanced and foundational topics. I recommend it either for personal use or for a class with students with strong quantitative backgrounds. The second edition has excellent coverage of exciting developments regarding analysis of longitudinal data. The chapter authors are a 'who's who' of the field.”

—Jeffrey S. Simons, PhD, Department of Psychology, University of South Dakota


“I have always recommended this book for anyone interested in conducting or teaching SEM. The second edition continues to be my favorite comprehensive SEM reference book, with new chapters covering advanced topics.”

—Dana Joseph, PhD, College of Business Administration, University of Central Florida

Table of Contents

I. Foundations

1. Structural Equation Modeling: An Overview, Rick H. Hoyle sample

2. A Brief History of Structural Equation Modeling, Ross L. Matsueda

3. The Causal Foundations of Structural Equation Modeling, Judea Pearl

4. Visualizations for Structural Equation Modeling, Jolynn Pek, Erin K. Davisson, & Rick H. Hoyle

5. Latent Variables in Structural Equation Modeling, Kenneth A. Bollen & Rick H. Hoyle

6. Simulation Methods in Structural Equation Modeling, Walter L. Leite, Deborah L. Bandalos, & Zuchao Shen

7. Assumptions in Structural Equation Modeling, Rex B. Kline

8. On the Estimation of Structural Equation Models with Latent Variables, Yunxiao Chen, Irini Moustaki, & Siliang Zhang

9. Power Analysis within a Structural Equation Modeling Framework, Yi Feng & Gregory R. Hancock

10. Model Fit in Structural Equation Modeling, Stephen G. West, Wei Wu, Daniel McNeish, & Andrea Savord

11. Model Selection in Structural Equation Modeling, Kristopher J. Preacher & Haley E. Yaremych

12. Fitting Structural Equation Models with Missing Data, Craig K. Enders

13. Structural Equation Modeling with the Mplus and lavaan Programs, Christian Geiser

II. Basic Models and Applications

14. Confirmatory Factor Analysis, Timothy A. Brown

15. Confirmatory Measurement Models for Dichotomous and Ordered Polytomous Indicators, Natalie A. Koziol

16. Item Parceling in SEM: A Researcher Degree-of-Freedom Ripe for Opportunistic Use, Sonya K. Sterba & Jason D. Rights

17. Using Factor Scores in Structural Equation Modeling, Ines Devlieger & Yves Rosseel

18. Bifactor Measurement Models, Steven P. Reise, Maxwell Mansolf, & Mark G. Haviland

19. Multitrait-Multimethod Models, Michael Eid, Tobias Koch, & Christian Geiser

20. Investigating Measurement Invariance Using Confirmatory Factor Analysis, Keith F. Widaman & Margarita Olivera-Aguilar

21. Flexible Structural Equation Modeling Approaches for Analyzing Means, Marilyn S. Thompson, Yixing Liu, & Samuel B. Green

22. Mediation/Indirect Effects in Structural Equation Modeling, Oscar Gonzalez, Matthew J. Valente, Jeewon Cheong, & David P. MacKinnon

23. Latent Interaction Effects, Augustin Kelava & Holger Brandt

24. Dynamic Moderation with Latent Interactions: General Cross-lagged Panel Models with Interaction Effects Over Time, Michael J. Zyphur & Ozlem Ozkok

25. Psychometric Scale Evaluation Using Structural Equation Modeling and Latent Variable Modeling, Tenko Raykov

26. Multilevel Structural Equation Modeling, Ronald H. Heck & Tingting Reid

III. Specialized and Advanced Models and Applications

27. Exploratory Structural Equation Modeling, Alexandre J. S. Morin

28. Structural Equation Modeling with Small Samples and Many Variables, Katerina M. Marcoulides, Ke-Hai Yuan, & Lifang Deng

29. Mixture Models, Douglas Steinley

30. Latent Curve Modeling of Longitudinal Growth Data, Kevin J. Grimm & John J. McArdle

31. Dynamic Structural Equation Modeling as a Combination of Time Series Modeling, Multilevel Modeling, and Structural Equation Modeling, Ellen L. Hamaker, Tihomir Asparouhov, & Bengt Muthén

32. Continuous-Time Dynamic Models: Connections to Structural Equation Models and Other Discrete-Time Models, Sy-Miin Chow, Diane Losardo, Jonathan Park, & Peter C. M. Molenaar

33. Latent Trait-State Models, David A. Cole & Qimin Liu

34. Longitudinal Models for Assessing Dynamics in Dyadic Data, Meng Chen, Hairong Song, & Emilio Ferrer

35. Structural Equation Modeling in Genetics, Susanne Bruins, Sanja Franić, Conor V. Dolan, Denny Borsboom, & Dorret I. Boomsma

36. Structural Equation Modeling (SEM)-Based Meta-Analysis, Mike W.-L. Cheung

37. Nonlinear Structural Equation Models: Advanced Methods and Applications, Jeffrey R. Harring & Jinwang Zou

38. Foundations and Extensions of Bayesian Structural Equation Modeling, Sarah Depaoli, David Kaplan, & Sonja D. Winter

39. Machine Learning Approaches to Structural Equation Modeling, Andreas M. Brandmaier & Ross C. Jacobucci


About the Editor

Rick H. Hoyle, PhD, is Professor of Psychology and Neuroscience and Director of the Center for the Study of Adolescent Risk and Resilience at Duke University. He is a Fellow of the Association for Psychological Science, the American Psychological Association (Divisions 1, 5, 8, and 9), and the Society for Experimental Social Psychology. Dr. Hoyle has written extensively on structural equation modeling and other statistical and methodological strategies for the study of complex social and behavioral processes.

Contributors

Tihomir Asparouhov, PhD, Muthén and Muthén, Los Angeles, CA

Deborah L. Bandalos, PhD, Department of Graduate Psychology, James Madison University, Harrisonburg, VA

Kenneth A. Bollen, PhD, Department of Psychology and Neuroscience and Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC

Dorret I. Boomsma, PhD, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Denny Borsboom, PhD, Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands

Andreas M. Brandmaier, PhD, Department of Psychology, MSB Medical School Berlin, and Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany

Holger Brandt, PhD, Methods Center, University of Tübingen, Tübingen, Germany

Timothy A. Brown, PsyD, Department of Psychological and Brain Sciences, Boston University, Boston, MA

Susanne Bruins, MSc, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Meng Chen, PhD, Department of Psychology, University of California, Davis, Davis, CA

Yunxiao Chen, PhD, Department of Statistics, London School of Economics and Political Science, London, UK

JeeWon Cheong, PhD, Department of Health Education and Behavior, University of Florida, Gainesville, FL

Mike W.-L. Cheung, PhD, Department of Psychology, National University of Singapore, Singapore

Sy-Miin Chow, PhD, College of Health and Human Development, Pennsylvania State University, University Park, PA

David A. Cole, PhD, Department of Psychology and Human Development, Vanderbilt University, Nashville, TN

Erin K. Davisson, PhD, Department of Psychology and Neuroscience, Duke University, Durham, NC

Lifang Deng, PhD, Department of Psychology, Beihang University, Beijing, China

Sarah Depaoli, PhD, Department of Psychology, University of California, Merced, Merced, CA

Ines Devlieger, PhD, Department Informatics and Technology, Howest University of Applied Sciences, Bruges, Belgium

Conor V. Dolan, PhD, Department of Biological Pyschology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Michael Eid, PhD, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany

Craig K. Enders, PhD, Department of Psychology, University of California, Los Angeles, Los Angeles, CA

Yi Feng, MA, Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD

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

Sanja Franić, PhD, private practice, London, UK

Christian Geiser, PhD, QuantFish LLC, Ronceverte, WV

Oscar Gonzalez, PhD, Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC

Samuel B. Green, PhD (deceased), School of Social and Family Dynamics, Arizona State University, Tempe, AZ

Kevin J. Grimm, PhD, Department of Psychology, Arizona State University, Tempe, AZ

Ellen L. Hamaker, PhD, Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, The Netherlands

Gregory R. Hancock, PhD, Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD

Jeffrey R. Harring, PhD, Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD

Mark G. Haviland, PhD, Department of Psychiatry, Loma Linda University School of Medicine, Loma Linda, CA

Ronald H. Heck, PhD, College of Education, University of Hawaii at Manoa, Honolulu, HI

Rick H. Hoyle, PhD, Department of Psychology and Neuroscience, Duke University, Durham, NC

Ross C. Jacobucci, PhD, Department of Psychology, University of Notre Dame, Notre Dame, IN

David Kaplan, PhD, Department of Educational Psychology, University of Wisconsin–Madison, Madison, WI

Augustin Kelava, PhD, Methods Center, University of Tübingen, Tübingen, Germany

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

Tobias Koch, PhD, Institut für Psychologie, Friedrich-Schiller-Universität Jena, Jena, Germany

Natalie A. Koziol, PhD, Nebraska Academy for Methodology, Analytics and Psychometrics, Nebraska Center for Research on Children, Youth, Families and Schools, University of Nebraska–Lincoln, Lincoln, NE

Walter L. Leite, PhD, College of Education, University of Florida, Gainesville, FL

Qimin Liu, MS, Department of Psychology and Human Development, Vanderbilt University, Nashville, TN

Yixing Liu, PhD, School of Management, Beijing University of Chinese Medicine, Beijing, China

Diane Losardo, PhD, Penguin Random House, New York, NY

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

Maxwell Mansolf, PhD, Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL

Katerina M. Marcoulides, PhD, Department of Psychology, University of Minnesota, Minneapolis, MN

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

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

Daniel McNeish, PhD, Department of Psychology, Arizona State University, Tempe, AZ

Peter C. M. Molenaar, PhD, College of Health and Human Development, Pennsylvania State University, University Park, PA

Alexandre J. S. Morin, PhD, Substantive–Methodological Synergy Research Laboratory, Department of Psychology, Concordia University, Montreal, Quebec, Canada

Irini Moustaki, PhD, Department of Statistics, London School of Economics and Political Science, London, UK

Bengt Muthén, PhD, Graduate School of Education and Information Studies, University of California, Los Angeles, and Muthén, Los Angeles, CA

Margarita Olivera-Aguilar, PhD, Educational Testing Service, Princeton, NJ

Ozlem Ozkok, PhD, Rennes School of Business, Rennes, France

Jonathan Park, MA, Department of Human Development and Family Studies, Pennsylvania State University, University Park, PA

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

Jolynn Pek, PhD, Department of Psychology, The Ohio State University, Columbus, OH

Kristopher J. Preacher, PhD, Department of Psychology and Human Development, Vanderbilt University, Nashville, TN

Tenko Raykov, PhD, Measurement and Quantitative Methods, Michigan State University, East Lansing, MI

Tingting Reid, PhD, Institute of Psychology, University of Hildesheim, Hildesheim, Germany

Steven P. Reise, PhD, Department of Psychology, University of California, Los Angeles, Los Angeles, CA

Jason D. Rights, PhD, Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada

Yves Rosseel, PhD, Department of Data Analysis, Ghent University, Ghent, Belgium

Andrea Savord, MS, Department of Psychology, Arizona State University, Tempe, Arizona

Zuchao Shen, PhD, Sanford School of Social and Family Dynamics, Arizona State University, Tempe, AZ

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

Douglas Steinley, PhD, Department of Psychological Sciences, University of Missouri, Columbia, MI

Sonya K. Sterba, PhD, Department of Psychology and Human Development, Vanderbilt University, Nashville, TN

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

Matthew J. Valente, PhD, College of Public Health, University of South Florida, Tampa, FL

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

Keith F. Widaman, PhD, School of Education, University of California, Riverside, Riverside, CA

Sonja D. Winter, PhD, Missouri Prevention Science Institute, University of Missouri, Columbia, MI

Wei Wu, PhD, Department of Psychology, Indiana University–Purdue University Indianapolis, Indianapolis, IN

Haley E. Yaremych, MS, Department of Psychology and Human Development, Vanderbilt University, Nashville, TN

Ke-Hai Yuan, PhD, Department of Psychology, University of Notre Dame, Notre Dame, IN

Siliang Zhang, PhD, Key Laboratory of Advanced Theory and Application in Statistics and Data Science–MOE, School of Statistics, East China Normal University, Shanghai, China

Jinwang Zou, Department of Human Development and Quantitative Methodology, University of Maryland College Park, College Park, MD

Michael J. Zyphur, PhD, UQ Business School, University of Queensland, Brisbane, Australia

Audience

Graduate students, instructors, researchers, administrators, and evaluators in psychology, education, human development and family studies, management, sociology, social work, nursing, public health, criminal justice, and communication. Readers will have had at least two graduate statistics course, such as multiple regression and factor analysis.

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.
Previous editions published by Guilford:

First Edition, © 2012
ISBN: 9781462516797
New to this edition: