Evaluating Avoidable Heterogeneity in Exploratory Factor Analysis Results

Meaningful interpretations of scores derived from psychological scales depend on the replicability of psychometric properties. Despite this, and unexpected inconsistencies in psychometric results across studies, psychometrics has often been overlooked in the replication literature. In this article, we begin to address replication issues in exploratory factor analysis (EFA). We use a Monte Carlo simulation to investigate methodological choices made throughout the EFA process that have the potential to add heterogeneity to results. Our findings show that critical decision points for EFA include the method for determining the number of factors as well as rotation. The results also demonstrate the relevancy of data characteristics, as some contexts are more susceptible to the effects of methodological choice on the heterogeneity of results.