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Structural Equation Modeling: Concepts, Issues, and Applications by Kenneth A. Bollen (Foreword), Rick H. Hoyle (Editor) (28-Feb-1995) Paperback
Excellent Book
Paperback
First published February 1, 1995
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About the author
Rick H. Hoyle
21 books1 followerRick 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).
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April 18, 2011
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.
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.
Displaying 1 of 1 review