An individual differences model for multiple regression

A model for multiple regression was developed which allows individual differences to emerge empirically. The model encompasses as special cases several of the previous attempts to improve psychological prediction by deviating from the usual linear multiple regression model. The model is tested with both artificial and real data. The results indicate that the model effectively reduces the variance of the error of prediction, and that the weights obtained are stable over different samples, and, to some extent, over different sets of predictors.This article is based upon a thesis submitted in partial fulfillment of the requirements for the doctoral degree at the University of Illinois. The author thanks Professor Ledyard R Tucker who served as committee chairman and offered considerable support and assistance.