A Cautionary Note on the Use of Principal Components Analysis

In a recent edition of this journal, Borgatta et al. (1986), using hypothetical data, illustrated how the results produced by principal components analysis can be substantially different from those of common factor analysis. The present article, using seven well-known data sets, extends their work into the empirical domain, and also compares the results of the maximum likelihood factor analysis model with those of the principal components model. The results strongly support those of Borgatta et al. Indeed, the discrepancies in the empirical results reported here are often larger than their hypothetical example suggests. It was found that, when comparing the performance of the principal components model with the common factor and maximum likelihood models, differences can be expected to occur in (1) the magnitudes of the factor loadings, (2) the signs attached to the factor loadings, and, most important, (3) the interpretation of the factors themselves.