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

Abstract

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 fulfill the requirements of the method. We investigated the influence of sample size, data transformation, factor extraction method, rotation, and number of factors on the outcome. We compared classical exploratory factor analysis with a robust counterpart which is less influenced by data outliers and data heterogeneities. Our analyses revealed that robust exploratory factor analysis is more stable than the classical method.