Comparison of Methods for Addressing Outliers in Exploratory Factor Analysis and Impact on Accuracy of Determining the Number of Factors

Abstract

Exploratory factor analysis (EFA) is a very common tool used in the social sciences to identify the underlying latent structure for a set of observed measurements. A primary component of EFA practice is determining the number of factors to retain, given the sample data. A variety of methods are available for this purpose, including parallel analysis, minimum average partial, and the Chi-square difference test. Research has shown that the presence of outliers among the indicator variables can have a deleterious impact on the performance of these methods for determining the number of factors to retain. The purpose of the current simulation study was to compare the performance of several methods for dealing with outliers combined with multiple techniques for determining the number of factors to retain. Results showed that using correlation matrices produced by either the percentage bend or heavy-tailed Student’s t-distribution, coupled with either parallel analysis or the minimum average partial yield, were most accurate in terms of identifying the number of factors to retain. Implications of these findings for practice are discussed.