Regularized Exploratory Factor Analysis as an Alternative to Factor Rotation

Abstract

Exploratory factor analysis (EFA) is widely used in psychological (assessment) research. Due to its exploratory nature, several researcher degrees of freedom exist on how to conduct the analysis. While simulation studies can provide meaningful insights into which factor retention methods to use to determine the number of latent factors, or which estimation methods recover parameter values most precisely given certain data characteristics, the issue of rotational indeterminacy makes it very difficult to decide which rotation method to apply. An alternative to the two-stage approach of extracting factors and subsequently rotating them to foster interpretability is the so-called regularized EFA. In this paper, we contrast both approaches and demonstrate how regularized EFA can be applied. In doing so, we want to encourage researchers to try out the approach themselves and help them find a way of EFA that appears less arbitrary compared to classical factor rotation.