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Volume 10 Number 7, July 2005
ISSN 1531-7714
Best Practices in Exploratory Factor Analysis: Four
Recommendations for Getting the Most From Your
Analysis
Anna B. Costello and Jason W. Osborne
North Carolina State University
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...