Despite advances in common factor analysis, a review of 89 studies published in four selected journals between 1963 and 1976 indicated that behavioral scientists preferred principal components analysis, followed by varimax or orthogonal rotation. Resultant row sums of squares of factor matrices from principal component analyses of real data sets had a relatively low correlation with similar matrices from the most widely used common factor methods. A slight generalization of Whittle's method of common factor analysis was shown to provide a number of distinctive advantages over component analyses and Joreskog's likelihood methods. Finally, guidelines were provided to assist prospective factor analytic users in deciding when their problems might best be apProached by common factor methods, and when other multivariate structural methods might be more effective or at least complementary. A Bayesian approach to these problems was advocated in this final section. (Author/CP)