Handbook of Structural Equation Modeling
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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 datasets, code, and output for many of the chapters, as well as bonus selected chapters from the prior edition.
New to This Edition
*Chapters on additional topics not mentioned above: SEM-based meta-analysis, dynamic SEM, machine-learning approaches, and more.
*Chapters include computer code associated with example analyses (in Mplus and/or the R package lavaan), along with written descriptions of results.
*60% new material reflects a decade's worth of developments in the mechanics and application of SEM.
*Many new contributors and fully rewritten chapters.
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.
“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
“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-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. (on the first edition)--Structural Equation Modeling, 4/1/2013