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A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling
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About A Step-by-Step Approach To Using SAS For Factor
Product Description Structural equation modeling (SEM) has become one of the most important statistical procedures in the social and behavioral sciences. This easy-to-understand guide makes SEM accessible to all userseven those whose training in statistics is limited or who have never used SAS. It gently guides users through the basics of using SAS and shows how to perform some of the most sophisticated data-analysis procedures used by researchers: exploratory factor analysis, path analysis, confirmatory factor analysis, and structural equation modeling. It shows how to perform analyses with user-friendly PROC CALIS, and offers solutions for problems often encountered in real-world research. This second edition contains new material on sample-size estimation for path analysis and structural equation modeling. In a single user-friendly volume, students and researchers will find all the information they need in order to master SAS basics before moving on to factor analysis, path analysis, and other advanced statistical procedures. Review This is an excellent, user-friendly, and readable text providing a step-by-step guide for learning and understanding measurement and structural equation modeling with SAS. The text begins with principle component analysis and exploratory factor analysis, and continues with path analysis, confirmatory factor analysis, and finally full structural equation models. (Appendices describe the basics for those new to SAS.) Each chapter addresses one of these methods. The chapters include applied examples and illustrate the syntax and output for each, providing both step-by-step procedures on how to undertake analyses and interpret findings. Chapters also include useful guidelines on how to report the results in a research paper. Furthermore, the book provides SAS users the methods for estimating sample size requirements and statistical power for path analyses, confirmatory factor analysis, and structural equation models. I warmly recommend this book to those who wish to explore the world of measurement and structural equation models, particularly (but not only) if they wish to do it using the SAS program. I would also strongly recommend the book to teachers and to social scientists already working in the field who wish to deepen and improve their understanding of statistical procedures. The book will prove itself to be an indispensable tool in these endeavors. This text is suitable for both senior undergraduate and graduate courses. --Eldad Davidov, University of Zurich, Switzerland About the Author Norm O'Rourke, Ph.D., R.Psych., is a clinical psychologist and associate professor with the Interdisciplinary Research in the Mathematical and Computational Sciences (IRMACS) Centre at Simon Fraser University in Burnaby (BC), Canada. He sits on the executive board of the American Psychological Association's Society for Clinical Geropsychology and the National Mental Health Commission of Canada. To date, he has published two governmental reports and seventy peer-reviewed publications in leading gerontology, measurement, and mental health academic journals. As co-applicant, Dr. O'Rourke has been part of teams awarded $4M in research funding, and $1.3M as principal applicant in governmental and foundation funding as team leader. Larry Hatcher, Ph.D., is a professor of psychology at Saginaw Valley State University in Saginaw, Michigan, where he teaches classes in general psychology, industrial psychology, statistics, and computer applications in data analysis. The author of several books dealing with statistics and data analysis, Larry has taught at the college level since 1984 after earning his doctorate in industrial and organizational psychology from Bowling Green State University in 1983.
