The Predictive Accuracy of Full-Rank Variables Vs. Various Types of Factor Scores: Implications for Test Validation

Abstract

A recent condemnation of the use of factor scores as predictors in multiple regression because of the loss in "predictive accuracy" incurred in reducing rank (Kukuk and Baty, 1979) was reexamined from the more important predictive perspective of replication predictive accuracy. Using a computer-based Monte Carlo procedure parallel to that employed in a recent comparison of various types of factor scores (Morris, 1979) the investigator compared the double cross-validation replication predictive accuracies of six types of factor scores with that of full-rank data by utilizing a data set from the literature in which classroom achievement was predicted from affective and cognitive variables. Prediction accuracy was significantly more accurate for each of the six types of factor scores than for full-rank data. Further, corroborative evidence was presented for the superiority of incomplete factor scores over the nonincomplete methods considered. Moreover, implications for reconsidering the sample size typically deemed necessary for factor analysis in predictive situations were offered.