Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most From Your Analysis (original) (raw)

Exploratory factor analysis (EFA) is a complex, multi-step process. The goal of this paper is to collect, in one article, information that will allow researchers and practitioners to understand the various choices available through popular software packages, and to make decisions about " best practices " in exploratory factor analysis. In particular, this paper provides practical information on making decisions regarding (a) extraction, (b) rotation, (c) the number of factors to interpret, and (d) sample size. Exploratory factor analysis (EFA) is a widely utilized and broadly applied statistical technique in the social sciences. In recently published studies, EFA was used for a variety of applications, including developing an instrument for the evaluation of school principals (Lovett, Zeiss, & Heinemann, 2002), assessing the motivation of Puerto Rican high school students (Morris, 2001), and determining what types of services should be offered to college students (Majors & Sedlacek, 2001). A survey of a recent two-year period in PsycINFO yielded over 1700 studies that used some form of EFA. Well over half listed principal components analysis with varimax rotation as the method used for data analysis, and of those researchers who report their criteria for deciding the number of factors to be retained for rotation, a majority use the Kaiser criterion (all factors with eigenvalues greater than one). While this represents the norm in the literature (and often the defaults in popular statistical software packages), it will not always yield the best results for a particular data set. EFA is a complex procedure with few absolute guidelines and many options. In some cases, options vary in terminology across software packages, and in many cases particular options are not well defined. Furthermore, study design, data properties, and the questions to be answered all have a bearing on which procedures will yield the maximum benefit. The goal of this paper is to discuss common practice in studies using exploratory factor analysis, and provide practical information on best practices in the use of EFA. In particular we discuss four issues: 1) component vs. factor extraction, 2) number of factors to retain for rotation, 3) orthogonal vs. oblique rotation, and 4) adequate sample size. BEST PRACTICE Extraction: Principal Components vs. Factor Analysis PCA (principal components analysis) is the default method of extraction in many popular statistical software packages, including SPSS and SAS, which likely contributes to its popularity. However, PCA is