Exploratory Factor Analysis With Small Samples and Missing Data

ABSTRACT

Exploratory factor analysis (EFA) is an extremely popular method for determining the underlying factor structure for a set of variables. Due to its exploratory nature, EFA is notorious for being conducted with small sample sizes, and recent reviews of psychological research have reported that between 40% and 60% of applied studies have 200 or fewer observations. Recent methodological studies have addressed small size requirements for EFA models; however, these models have only considered complete data, which are the exception rather than the rule in psychology. Furthermore, the extant literature on missing data techniques with small samples is scant, and nearly all existing studies focus on topics that are not of primary interest to EFA models. Therefore, this article presents a simulation to assess the performance of various missing data techniques for EFA models with both small samples and missing data. Results show that deletion methods do not extract the proper number of factors and estimate the factor loadings with severe bias, even when data are missing completely at random. Predictive mean matching is the best method overall when considering extracting the correct number of factors and estimating factor loadings without bias, although 2-stage estimation was a close second.

Notes

¹ Data with a very small percentage of missing values (e.g., 1%) contribute to the designation of LD as the most common method. This study did not consider conditions below 5% missingness, so we do not intend to imply that using LD with very small amounts of missing data will produce catastrophic results, especially because 1% or 2% missingness with the small samples of interest here corresponds to only a few people at most.