Average Reviews:
(More customer reviews)This book fills an important niche, a sweet spot in between basic SEM texts such as Rex Kline's helpful beginner's book and more complex, advanced treatments of SEM such as Ken Bollen's classic 1989 Wiley text. Methodologists might argue that the latter text is intermediate rather than advanced, but practitioners and applied users of SEM who are not in the business of creating new methods but instead want to use SEM in a rigorous, productive way on applied analysis problems will find this text to be just the ticket to getting things done using SEM and tackling typical problems such as how to handle missing data and how to calculate power for goodness-of-fit tests and parameter estimates.
The editors have done a terrific job in working with the chapter authors to make all chapters accessible with helpful examples and consistent notation and terminology. This book, along with Loehlin's Latent Variable Models, is one I find myself pulling off my bookcase repeatedly to solve applied problems or to learn more about a particular SEM issue (modeling options for complex survey data, mixture modeling) quickly, yet comprehensively. Highly recommended.
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Structural Equation Modeling: A Second Course (Quantitative Methods in Education and the Behavioral Science)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 EditorRonald C. Serlin, University of Wisconsin-Madison
This volume is intended to serve as a didactically-oriented resource covering a broad range of advanced topics often not discussedin introductory courses on structural equation modeling (SEM). Such topics are important in furthering the understandingof foundations and assumptions underlying SEM as well as in exploring SEM as a potential tool to address new types ofresearch questions that might not have arisen during a first course. Chapters focus on the clear explanation and application oftopics, 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 & FrankR. Lawrence. Mean and Covariance Structure Mixture Models, Phill Gagné. Structural Equation Models of Latent Interactionand Quadratic Effects, Herbert W. Marsh, Zhonglin Wen, & Kit-Tai Hau. Part III: Assumptions. Nonnormal and CategoricalData in Structural Equation Modeling, Sara J. Finney & Christine DiStefano. Analyzing Structural Equation Models withMissing 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. Aboutthe Authors.
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