The next step toward the full factor analysis model is a technique known as principal axis factors (PAF), or sometimes also called iterated principal axis factors, or just principal factors. The principal factors model is a blend of the principal components model described earlier and the full common factor model. In the common factor model, each of the original variables is described in terms of certain underlying or common factors, as well as a unique factor for that variable. In principal axis factors, however, each variable is described in terms of common factors without a unique factor.
Unlike a principal components model for which there is a unique solution, a principal axis factor model consists of estimated factors and scores. As with principal components, the derived factors are orthogonal or independent of each other. The same is not necessarily true of the scores, however. Refer to Factor Scores for more information.