Comparison of the Performance of Varimax and Promax Rotations: Factor Structure Recovery for Dichotomous Items

Abstract

Nonlinear factor analysis is a tool commonly used by measurement specialists to identify both the presence and nature of multidimensionality in a set of test items, an important issue given that standard Item Response Theory models assume a unidimensional latent structure. Results from most factor‐analytic algorithms include loading matrices, which are used to link items with factors. Interpretation of the loadings typically occurs after they have been rotated in order to amplify the presence of simple structure. The purpose of this simulation study is to compare the ability of two commonly used methods of rotation, Varimax and Promax, in terms of their ability to correctly link items to factors and to identify the presence of simple structure. Results suggest that the two approaches are equally able to recover the underlying factor structure, regardless of the correlations among the factors, though the oblique method is better able to identify the presence of a “simple structure.” These results suggest that for identifying which items are associated with which factors, either approach is effective, but that for identifying simple structure when it is present, the oblique method is preferable.