Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research

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.