Full Description
Sponsored by the American Educational Research Association's Special Interest Group for Educational Statisticians
This volume is the second edition of Hancock and Mueller's highly-successful 2006 volume, with all of the original chapters updated as well as four new chapters. The second edition, like the first, is intended to serve as a didactically-oriented resource for graduate students and research professionals, 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 materials 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.
Chapter 2. The Problem of Equivalent Structural Models, Scott L. Hershberger & George A. Marcoulides.
Chapter 3. Reverse Arrow Dynamics: Feedback Loops and Formative Measurement, Rex B. Kline.
Chapter 4. Partial Least Squares Path Modeling, Edward E. Rigdon.
Chapter 5. Power Analysis in Structural Equation Modeling, Gregory R. Hancock & Brian F. French.
Part II. Extensions.
Chapter 6. Evaluating Between-Group Differences in Latent Variable Means, Marilyn S. Thompson & Samuel B. Green.
Chapter 7. Conditional Process Modeling: Using Structural Equation Modeling to Examine Contingent Causal Processes, Andrew F. Hayes & Kristopher J. Preacher.
Chapter 8. Structural Equation Models of Latent Interaction and Quadratic Effects, Herbert W. Marsh, Zhonglin Wen, Kit-Tai Hau, & Benjamin Nagengast.
Chapter 9. Using Latent Growth Modeling to Evaluate Longitudinal Change, Gregory R. Hancock, Jeffrey R. Harring, & Frank R. Lawrence.
Chapter 10. Mean and Covariance Structure Mixture Models, Dena A. Pastor & Phill Gagné.
Chapter 11. Exploratory Structural Equation Modeling, Alexandre J. S. Morin, Herbert W. Marsh, & Benjamin Nagengast.
Part III. Assumptions.
Chapter 12. Nonnormal and Categorical Data in Structural Equation Modeling, Sara J. Finney & Christine DiStefano.
Chapter 13. Analyzing Structural Equation Models with Missing Data, Craig K. Enders.
Chapter 14. Multilevel Structural Equation Modeling with Complex Sample Data, Laura M. Stapleton.
Chapter 15. Bayesian Structural Equation Modeling, Roy Levy & Jaehwa Choi.
Chapter 16. Use of Monte Carlo Studies in Structural Equation Modeling Research, Deborah L. Bandalos & Walter Leite.
About the Authors.
