Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis Review

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(More customer reviews)I read this book's earlier edition so that I know this book is a good one. I bought a kindle version of it and found that the 4th edition is meeting my expectation. Only troubling issue that I found from this kindle version is that the data cd coming with a paper version of this book is missing in kindle version. Neither the publisher nor the amazon.com provide a link to the data cd. My complain to amazon.com is that they didn't inform their customer about this.

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This book introduces multiple-latent variable models by utilizing path diagrams to explain the underlying relationships in the models. This approach helps less mathematically inclined students grasp the underlying relationships between path analysis, factor analysis, and structural equation modeling more easily. A few sections of the book make use of elementary matrix algebra. An appendix on the topic is provided for those who need a review. The author maintains an informal style so as to increase the book's accessibility. Notes at the end of each chapter provide some of the more technical details. The book is not tied to a particular computer program, but special attention is paid to LISREL, EQS, AMOS, and Mx. New in the fourth edition of Latent Variable Models: *a data CD that features the correlation and covariance matrices used in the exercises; *new sections on missing data, non-normality, mediation, factorial invariance, and automating the construction of path diagrams; and *reorganization of chapters 3-7 to enhance the flow of the book and its flexibility for teaching. Intended for advanced students and researchers in the areas of social, educational, clinical, industrial, consumer, personality, and developmental psychology, sociology, political science, and marketing, some prior familiarity with correlation and regression is helpful.

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Structural Equation Modeling with LISREL: Essentials and Advances Review

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(More customer reviews)The book is best at presenting the theory and has excellent discussions of advanced estimation issues. The book, however, is not as useful in conveying the nuts and bolts of how to use LISREL.

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Hayduk is equally at ease explaining the simplest and most advanced applications of the program . . . Hayduk has written more than just a solid text for use in advanced graduate courses on statistical modeling. Those with a firm mathematical background who wish to learn about the approach, or those who know a little about the program and want to know more, will find this an excellent reference.

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Structural Equation Modeling: A Second Course (Quantitative Methods in Education and the Behavioral Science) Review

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(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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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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A First Course in Structural Equation Modeling Review

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(More customer reviews)This book does a good job of explaining the basic concepts involved in SEM, path analysis, and CFA in easy to understand terms. Also, it lays out syntax and output for running popular SEM programs, such as LISREL, EQS, and Mplus. This book is perfect for the person who is trying to learn SEM on their own. I think it gives you enough to be able to know how to run a SEM, but it isn't enough to really make you a pro at it.

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In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one.Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner's guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software.Highlights of the Second Edition include:' Review of latent change (growth) analysis models at an introductory level' Coverage of the popular Mplus program' Updated examples of LISREL and EQS' A CD that contains all of the text's LISREL, EQS, and Mplus examples.A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful.

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