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Exploratory Factor Analysis (Understanding Statistics), by Leandre R. Fabrigar, Duane T. Wegener
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Exploratory Factor Analysis (EFA) has played a major role in research conducted in the social sciences for more than 100 years, dating back to the pioneering work of Spearman on mental abilities. Since that time, EFA has become one of the most commonly used quantitative methods in many of the social sciences, including psychology, business, sociology, education, political science, and communications. To a lesser extent, it has also been utilized within the physical and biological sciences. Despite its long and widespread usage in many domains, numerous aspects of the underlying theory and application of EFA are poorly understood by researchers. Indeed, perhaps no widely used quantitative method requires more decisions on the part of a researcher and offers as wide an array of procedural options as EFA does.
This book provides a non-mathematical introduction to the underlying theory of EFA and reviews the key decisions that must be made in its implementation. Among the issues discussed are the use of confirmatory versus exploratory factor analysis, the use of principal components analysis versus common factor analysis, procedures for determining the appropriate number of factors, and methods for rotating factor solutions. Explanations and illustrations of the application of different factor analytic procedures are provided for analyses using common statistical packages (SPSS and SAS), as well as a free package available on the web (Comprehensive Exploratory Factor Analysis). In addition, practical instructions are provided for conducting a number of useful factor analytic procedures not included in the statistical packages.
- Sales Rank: #844543 in Books
- Published on: 2011-12-22
- Released on: 2011-12-22
- Original language: English
- Number of items: 1
- Dimensions: 5.50" h x .70" w x 8.20" l, .49 pounds
- Binding: Paperback
- 176 pages
About the Author
Leandre R. Fabrigar is Associate Professor of Psychology at Queen's University. He received his PhD in Psychology with a minor in Quantitative Psychology in 1995 from the Ohio State University. His primary research and teaching falls within the domains of attitudes, quantitative methods (including factor analysis and structural equation modeling), and psychological measurement. His research has been supported by grants from the Social Science and Humanities Research Council of Canada, Canadian Institutes of Health Research, and the Ontario Problem Gambling Research Centre.
Duane T. Wegener is Professor of Psychology at the Ohio State University and held previous faculty positions at Yale University and Purdue University. He received his PhD in Psychology with a minor in Quantitative Psychology in 1994 from Ohio State University. His primary research and interests are in attitudes and social cognition. He also teaches undergraduate and graduate statistics and research methods. His research has been supported by grants from the U. S. National Science Foundation, Canadian Institutes of Health Research, and U.S. National Institutes of Health. He received the Early Career Award from the American Psychological Association in 2001 for contributions to social psychology.
Most helpful customer reviews
7 of 7 people found the following review helpful.
Great applied introduction and "how to" book for EFA
By John Sakaluk
Fabrigar and Wegner have put together a short and sweet, but very effective introduction to exploratory factor analysis. The language used will be accessible to most readers with basic statistics training, and the book can be read front-to-back in an afternoon or two. The book places an emphasis on the application of EFA, and in that way can be thought of as a good "how to" resource of best practices in EFA. Nevertheless, the book does include a basic introduction to the conceptual math behind EFA for those who are looking for that sort of thing. Those with a strong background in quantitative psychology may not be satiated with this introduction, but for most practitioners of EFA, this material provides a brief, valuable and relatively harmless introduction of the matrix algebra involved in EFA. For those who have read Fabrigar's (1999) "Evaluating the use of exploratory factor analysis in psychological research", the book contains a many of the same recommendations. Readers will also likely enjoy the walk-through that the authors provide for adhering to these best practices in SPSS, SAS and CEFA (CEFA is a freely available EFA program; a great find for poor, struggling undergraduate/graduate students).
There were a few nit-picky aspects about the book that left me a bit disappointed. I would have liked the authors to have included Mplus in the section of EFA-able software packages they reviewed. EFA is Mplus is dead-simple to do, gives comprehensive output, and in some ways (in my opinion) is more powerful that SPSS/SAS/CEFA for EFA. One particular feature of Mplus that I think would have been valuable to cover is the use of robust maximum-likelihood estimators in EFA, which can help with some of the problems researchers run into when they try to conduct an EFA using indicators with normality problems.
A possibly more important critique, given this book's apparent target audience (i.e., psychology/social science researchers using EFA), is the absence of an example EFA results write-up. With so many analytical decisions to make, and so much output to consider, I could see many students/faculty wishing they had a good model to follow regarding the write up of their EFA results. I imagine this omission could be easily remedied by the authors in the future, by uploading an example results section to the book's website. To be clear, the book is great, but I do hope they consider this small addition to the book's website; I think it could really increase the impact their book would have. Researchers would not only be conducting quality EFAs, they would also be writing quality result sections.
In summary, I think Fabrigar and Wegener have written a fabulous book that would be valuable to any student or researcher who plans on using EFA. Advanced statisticians may not find it to be a comprehensive statistics bible, but then again, it's not intended to be. Instead, the book provides a quick and easy one-stop read for how to conduct a quality EFA in a variety of statistical packages, and how to interpret the results.
1 of 1 people found the following review helpful.
Additional Info
By Richard C.
Great resource to compliment Pett et al., Field, or others when diving into FA especially for triangulating opinions on more than just procedures (i.e. skewness, kurtosis, etc.).
0 of 0 people found the following review helpful.
Only OK. Clear, but Redundant
By Dennis Hanseman
I was looking for a short, high-level review of exploratory factor analysis. This book satisfied my need fairly well. At 153 pages, it was definitely short. The problem is that it could well have been 100 pages long and done as well. These authors are verbose and repetitive in the extreme. Just as a quick example, each chapter begins with a rather long overview of what will be covered in that chapter. The entire final chapter reviews what was covered in the previous 142 pages. And certain issues are introduced, reviewed, re-reviewed, re-re-reviewed throughout the body of the text. The authors seem to assume that their readers have quite short attention spans!
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