Structural Equation Modeling: A Second Course

✍ Scribed by Gregory R. Hancock, Ralph O. Mueller

• Publisher: Information Age Publishing, Inc.
• Year: 2006
• Tongue: English
• Leaves: 446
• Series: Quantitative Methods in Education and the Behavioral Science
• Category: Library

✦ Synopsis

A volume in Quantitative Methods in Education and the Behavioral Sciences: Issues, Research, and Teaching (sponsored by the American Educational Research Association’s Special Interest Group: Educational Statisticians) Series Editor Ronald C. Serlin, University of Wisconsin-MadisonThis volume is intended to serve as a didactically-oriented resource covering a broad range of advanced topics often not discussed in introductory courses on structural equation modeling (SEM). Such topics are important in furthering the understanding of foundations and assumptions underlying SEM as well as in exploring SEM as a potential tool to address new types of research questions that might not have arisen during a first course. Chapters focus on the clear explanation and application of topics, rather than on analytical derivations, and contain syntax and partial output files from popular SEM software. CONTENTS: Introduction to Series, Ronald C. Serlin. Preface, Richard G. Lomax. Dedication. Acknowledgements. Introduction, Gregory R. Hancock & Ralph O. Mueller. Part I: Foundations. The Problem of Equivalent Structural Models, Scott L. Hershberger. Formative Measurement and Feedback Loops, Rex B. Kline. Power Analysis in Covariance Structure Modeling, Gregory R. Hancock. Part II: Extensions. Evaluating Between-Group Differences in Latent Variable Means, Marilyn S. Thompson & Samuel B. Green. Using Latent Growth Models to Evaluate Longitudinal Change, Gregory R. Hancock & Frank R. Lawrence. Mean and Covariance Structure Mixture Models, Phill Gagn?. Structural Equation Models of Latent Interaction and Quadratic Effects, Herbert W. Marsh, Zhonglin Wen, & Kit-Tai Hau. Part III: Assumptions. Nonnormal and Categorical Data in Structural Equation Modeling, Sara J. Finney & Christine DiStefano. Analyzing Structural Equation Models with Missing Data, Craig K. Enders. Using Multilevel Structural Equation Modeling Techniques with Complex Sample Data, Laura M. Stapleton. The Use of Monte Carlo Studies in Structural Equation Modeling Research, Deborah L. Bandalos. About the Authors.

✦ Table of Contents

Title
......Page 2
Copyright
......Page 3
Contents
......Page 4
Series introduction
......Page 8
Preface
......Page 10
Acknowledgments
......Page 14
1. Introduction
......Page 16
I. Foundations
......Page 26
2. The problem of equivalent structural models
......Page 28
3. Reverse arrow dynamics: Formative measurement and feedback loops
......Page 58
4. Power analysis in covariance structure modeling
......Page 84
II. Extensions
......Page 132
5. Evaluating between-group differences in latent variable means
......Page 134
6. Using latent growth models to evaluate longitudinal change
......Page 186
7. Mean and covariance structure mixture models
......Page 212
8. Structural equation models of latent interaction and quadratic effects
......Page 240
III. Assumptions
......Page 282
9. Non-normal and categorical data in structural equation modeling
......Page 284
10. Analyzing structural equation models with missing data
......Page 330
11. Using multilevel structural equation modeling techniques with complex sample data
......Page 360
12. The use of Monte Carlo studies in structural equation modeling research
......Page 400
About the authors
......Page 442