In this chapter we examined the concept of rotation — the purpose of rotation, what actually rotates, and several methods for rotation. We also examined the rotation results among each of our three example data sets. When the data was clear with a strong factor structure (as in examples1 and 2, with the engineering and SDQ data), almost any rotation will do a good job of clarifying the factor structure. We argued that oblique rotations performed slightly better given that the hypothetical factors were correlated in each of our data sets. However, if the factors were truly uncorrelated, the orthogonal and oblique rotations should identify very similar solutions. Finally, although the pattern and structure coefficients were reported, as this was an oblique rotation, the results were clear and, thus, the oblique rotation did not overly complicate the results.

In the third example (GDS data), the way forward was less clear. We explored a single-factor model, which might ultimately be the most desirable, given our preference for parsimony. However, in this model, the communalities were lower than ideal, and the overall variance accounted for was relatively low. Guidelines that we previously explored recommended extracting either three, five, or eight factors, but none of them seemed to make more sense to us than the single-factor model (you will have to decide whether the three-factor solution is the best or not). This scale might need a larger sample, revision, or it might need to be examined in the context of confirmatory methods in order to determine which model might be superior. However, we cannot just be guided by empirical data. The latent variables that we construct have to make sense.

At the end of the day, EFA is about empirically constructing a narrative that makes sense. Of all the models, see whether you can come up with one that makes more sense, rather than simply saying that all the items measure depression.