Exploratory factor analysis (EFA) serves many useful purposes in human resource development (HRD) research. The most frequent applications of EFA among researchers consists of reducing relatively large sets of variables into more manageable ones, developing and refining a new instrument’s scales, and exploring relations among variables to build theory. Because researchers face a number of decisions when conducting EFA that can involve some subjectivity (e.g., factor extraction method, rotation), poor analytic decisions regarding how the EFA should be conducted (e.g., number of factors to extract) can produce misleading findings to the detriment of these efforts, especially theory building.
The Solution
Steps must be taken to improve the quality of the decision making associated with conducting EFAs if sound theory building and research related to this statistical method is to continue. Higher quality EFAs facilitate higher quality theory building and research.
The Stakeholders
HRD theorists, researchers, and scholar-practitioners are the intended audience of this article. In particular, those interested in refining measures and theory building would benefit most from being exposed to best EFA decision-making practices.
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