"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
(Structural Equation Modeling
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.