Many of us, even those with years of experience using EFA, remain unclear on some of the nuances and details of what exactly is happening “under the hood” when we perform this analysis. Sticking with the default settings in most modern statistical software will generally not lead to using best practices. In PROC FACTOR, the default method of extraction is actually not even a method of factor analysis — it’s PCA! This is a solid choice if you were a psychologist in the 1960s, but in the 21st century, we can do better. So, pay attention: the extraction method must be specified in order for a factor analysis to be conducted.

As for which extraction method to use—our examples in this chapter demonstrated that most extraction techniques can be used when the data has a clear factor structure and meets basic assumptions of normality. When the data does not meet assumptions of normality, the iterated PAF or ULS extraction techniques can provide the best estimates. This led us to conclude that extraction method can matter more when assumptions are violated and less when assumptions are met.

However, there is general consensus in the literature that ML is the preferred choice for when data exhibits multivariate normality and iterated PAF or ULS for when that assumption is violated (Fabrigar, Wegener, MacCallum, & Strahan, 1999; Nunnally & Bernstein, 1994). Other extraction techniques seem to be vulnerable to violations of this assumption, and do not seem to provide any substantial benefit. Thus, the general recommendation to use either ML, iterated PAF, or ULS seems sensible.