What is it about?
Exploratory factor analysis is commonly used in IS research to detect multivariate data structures. Frequently, the method is blindly applied without checking if the data at hand fulfill the requirements of the method. In this paper, we investigate the influence of sample size, data transformation, factor extraction method, rotation and number of factors on the outcome. We compare classical exploratory factor analysis with a robust counterpart which is less influenced by data outliers and data heterogeneities. Our analyses reveal that robust exploratory factor analysis is more stable than the classical method.
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Why is it important?
In this paper we illustrate the various options researchers have when applying exploratory factor analysis (EFA). Additionally, we compare classical EFA with robust EFA.
Perspectives
This paper should be helpful for all researchers who consider to apply exploratory factor analysis (EFA) in their research. Frequently, this technique is applied blindly without taking into account the consequences. In this paper we briefly comment on the many options one has when it comes to applying EFA and also introduce its robust version, which is less influenced by data outliers and data heterogeneities.
Dr. Horst Treiblmaier
MODUL University Vienna
Read the Original
This page is a summary of: Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research, Information & Management, May 2010, Elsevier,
DOI: 10.1016/j.im.2010.02.002.
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