Abstract
Rotation of axes, which is almost routinely used in exploratory factor analysis, is not so common in principal component analysis (PCA) excepting in the fields such as climatology and psychology. In applying rotation to PCA we have to decide how to normalize the eigenvectors and which of the component coefficients or the component loadings to rotate, where the component coefficients are the coefficients for the original variables in computing the component scores and the component loadings are the coefficients for the component scores in approximating the original variables. These problems were studied numerically by applying these methods of orthogonal rotation to two actual data sets and several artificial data sets generated following factor analysis models. It was found that orthogonal rotation of component loadings tends to provide the result easier to interpret than that of the orthogonal rotation of component coefficients.
