The Superiority of Factor Scores as Predictors

Common factor scores were compared to unfactored data-level variables as predictors in terms of the correlation of a criterion with the predicted value in multiple regression equations applied to replication (cross-validation) samples. Data were generated by computer to provide populations with three different degrees of common variance inherent in their predictor variable intercorrelation matrices. Two replication populations differing from the original by specified amounts in their intercorrelation matrices were created for each common variance level. Results indicated that shrinkage was less for factor scores than for data-level variables for all combinations of common variance and difference of replication population. Moreover, the actual correlation describing accuracy of prediction was higher for factor scores than for data-level variables at the extreme conditions of common variance and difference of replication population.