A Comparison of Regression Prediction Accuracy on Several Types of Factor Scores

Abstract

Several advantages to the use of factor scores as independent variables in a multiple regression equation have been advocated in the literature. To provide guidance for selecting the most desirable type of factor score upon which to calculate a regression equation, computer-based Monte Carlo methods were used to compare the predictive accuracy upon replication of regression on five “complete” and four “incomplete” factor score estimation methods. For several levels of multiple correlation (R² = .30, .50, and. 70), and for several subject-to-variable sampling ratios (3:1, 5:1, and 10:1), prediction on incomplete factor scores showed better double cross-validated prediction accuracy than on complete factor scores. Moreover, the unique unit-weighted factor score was superior among the incomplete methods.