Exploratory Factor Analysis
Abstract
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 Charles 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 EFA versus confirmatory factor analysis, the use of principal component 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, as well as a free package available on the web. In addition, practical instructions are provided for conducting a number of useful factor analytic procedures not included in the statistical packages.
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Parallel Analysis showed that one factor had to be retained because only one factor had an eigenvalue of 9.08, greater than the 95th percentile eigenvalue of 1.53 for randomly generated data.
“Parallel Analysis [40] showed that one factor had to be retained because only one factor had an eigenvalue (raw data eigenvalue = 9.08) greater than the eigenvalue at the 95th percentile for randomly generated data (95th percentile eigenvalue = 1.53) [48].”
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- Authors
- Fabrigar, L. R., & Wegener, D. T.
- Affiliations
- Queen's University (CA)
- The Ohio State University (US)
- ORCID
- Wegener, Duane T.
- Venue
- Oxford University Press eBooks
- Publisher
- Oxford University Press
- Published
- 2011-12-22
- OpenAlex
- W62989984
- Cited by
- 1,408
- References
- 0
- Citations by year
- 2026: 95 · 2025: 149 · 2024: 121 · 2023: 105 · 2022: 108 · 2021: 118 · 2020: 102 · 2019: 98 · 2018: 84 · 2017: 72 · 2016: 50 · 2015: 76 · 2014: 76 · 2013: 65 · 2012: 32
- FWCI
- 21.33
- Type
- book
- Language
- es
- Open access
- closed
- Locations
- Oxford University Press eBooks (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- exploratory factor analysis, factor rotation, number of factors, principal component analysis, common factor analysis, confirmatory factor analysis, social sciences
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