Conventional wisdom in the literature and many texts advise researchers to use orthogonal rotation because it produces more easily interpretable results, but this might be a flawed argument. In the social sciences (and many other sciences), we generally expect some correlation among factors, particularly scales that reside within the same instrument or questionnaire (i.e., shared method variance will generally produce nonzero correlations). In practice, even when we create factors using an orthogonal method, the factor scores (scores derived from the factor structure; see Chapter 9) are often correlated despite the orthogonal nature of the factors. Therefore, using orthogonal rotation results in a loss of valuable information if the factors are really correlated, and oblique rotation should theoretically render a more accurate, and perhaps more reproducible, solution.[5] Further, in the unlikely event that researchers manage to produce truly uncorrelated factors, orthogonal and oblique rotation produce nearly identical results, leaving oblique rotation a very low-risk, potentially high-benefit choice.

The two sets of methods—orthogonal and oblique—do, however, differ in ease of interpretation. When using orthogonal rotation, researchers have only one matrix to interpret. When using oblique rotations, there are two matrices of results to review (described in the next section). In our experience—and in many of our examples—the two matrices tend to parallel each other in interpretation. So again, in our mind this does not create an insurmountable barrier.