Structural Equation Modeling: Concepts, Issues, and Applications (February 28, 1995) Paperback

by Rick H. Hoyle

Practical and up-to-date, Structural Equation Modeling includes chapters on major aspects of the structural equation modeling approach to research design and data analysis. Written by internationally recognized leaders in structural equation modeling, this book targets graduate students and seasoned researchers in the social and behavioral sciences who wish to understand the basic concepts and issues associated with the structural equation modeling approach and applications to research problems. Though technically sound, the chapters are primarily nontechnical in content and stylemaking the volume an excellent introduction to the structural equation modeling approach for readers studied in traditional inferential statistics. Early chapters are devoted to fundamental concepts such as estimation, fit, assumptions, power, and inference. Later chapters address such practical issues as the use of computer programs for applying the approach to research questions in the social and behavioral sciences.

Unknown Binding

First published February 1, 1995

About the author

Rick H. Hoyle, Ph.D., is Professor of Psychology and Neuroscience at Duke University. He is a Fellow of the American Psychological Association (Divisions 5, Evaluation, Measurement, and Statistics, and 9, Society for the Psychological Study of Social Issues) and a Fellow and Charter Member of the Association for Psychological Science. Dr. Hoyle has served as Associate Editor of the Journal of Personality and Social Psychology, Journal of Personality, and Self and Identity, and Editor of Journal of Social Issues. Among his book projects are, Selfhood: Identity, Esteem, Regulation (coauthored with Michael Kernis, Mark Leary, and Mark Baldwin) and the Handbook of Individual Differences in Social Behavior (co-edited with Mark Leary).

Reviews

[Steven Peterson · Rating 4 out of 5]

A fine work on structural equation modeling (SEM). This is a technique that allows one to develop path models coupled with confirmatory factor analysis (in its full and most useful form) to predict phenomena. This book has some nice essays in it, and I have used this as one tool by which to master SEM.

Now, there are a number of software packages that allow one to use this technique. My personal choice? AMOS (Analysis of Moment Structures for those who care!). I have analyzed a number of data sets with this program, and it provides satisfactory analysis.

Some advantages of SEM? There are a variety of "measures of fit," showing how well one's model describes the data. This is a substantial advantage over other prediction techniques, like regression, which have some fit statistics--but nothing like SEM.

Among the most useful chapters in this collection of essay: Hoyle's Introduction to SEM; Chou and Bentler on tests in SEM; Hu and Bentler's excellent essay on model fit and its evaluation (one of my most cited references when I use SEM in research); and so on.

There are several works that do a nice job outlining SEM. This volume was published in 1995, so it is a decade and a half old. Still, it is a solid work.