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E-raamat: Structural Equation Modeling

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Hancock (measurement, statistics, and evaluation, U. of Maryland) and Mueller (educational research, public policy, and public administration, George Washington U.) offer a volume to go along with introductory textbooks on structural equation modeling for the teaching of statistics in the social sciences. They bring together 12 chapters in which foundations not usually included in these texts are discussed--equivalent models, reverse arrow dynamics, and power analysis--as well as advanced topics such as latent variable means, latent growth, mixture, and nonlinear models. The final section deals with non-normal and categorical data, missing and multi-level data, and simulation studies. Contributors are internationally based and in fields such as statistics, educational psychology, and psychology. No index is supplied. Annotation ©2006 Book News, Inc., Portland, OR (booknews.com)

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I believe that this volume represents a vital contribution to the field of SEM beyond the introductory level. From the Preface by Richard G. Lomax, The University of Alabama

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 Gagne; 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.
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