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 multiva- riate 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 investi- gate the influence of sample size, data transformation, factor extraction method, rotation and number of factors on the outcome. We compare classical explorato- ry factor analysis with a robust counterpart which is less influenced by data out- liers and data heterogeneities. Our analyses reveal that robust exploratory factor analysis is more stable than the classical method.