A Beginner's Guide to Structural Equation Modeling, Volume 1

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Lawrence Erlbaum Associates, 2004 - Education - 498 pages
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This best-selling book introduces readers to the building blocks of structural equation modeling (SEM) so they can conduct their own analysis and understand and critique related research. Utilizing an application-oriented approach, each chapter covers basic concepts, principles, and practices, and then utilizes SEM software to provide meaningful examples. Most chapters follow the SEM basic steps: specification, identification, estimation, testing, and modification. A checklist is included to guide the reader's model analysis according to the basic steps a researcher takes. The text includes numerous examples using the latest versions of Amos (5.0), EQS (6.1), and LISREL (8.54). The book's early chapters are critical to understanding how missing data, non-normality, scale of measurement, non-linearity, outliers, and restriction of range in scores affect SEM analysis. Chapters 6 through 10 follow the basic SEM steps of modeling using regression, path, confirmatory factor, and structural equation models. Chapter 12 introduces several approaches to model validation, the final step in obtaining an acceptable theoretical model. The book concludes with a description of the matrix approach to SEM by using examples from the previous chapters. The second edition features:
*a CD with all of the book's Amos, EQS, and LISREL programs and data sets;
*new chapters on importing data issues related to data editing and on how to report research;
*an updated introduction to matrix notation and programs that illustrate how to compute these calculations;
*many more computer program examples and chapter exercises; and
*increased coverage of factors that affect correlation, the 4-step approach to SEM and hypothesis testing, significance, power, and sample size issues. The new edition's expanded use of applications make this book ideal for advanced students and researchers in psychology, education, business, health care, political science, sociology, and biology. A basic understanding of correlation is assumed and an understanding of the matrices used in SEM models is encouraged.

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About the author (2004)

Randall E. Schumacker is a Professor of Education Research at the University of North Texas. He has served on the editorial boards of numerous journals and is Emeritus Editor of Structural Equation Modeling.
Richard G. Lomax is a Professor of Education and Applied Statistics at the University of Alabama. He has published numerous artciles and presented numerous papers on related topics.

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