Developing prediction weights by matching battery factorings

Between-sample shrinkage of validity from sample errors is compounded when usual multiple regression techniques are employed to estimate weights for new battery components. A rationale is described for increasing prediction weight validity through a combination of a reduced-rank regression technique and a method for determining maximal factored congruence between two sets of measures. A numerical illustration is based on data drawn from a problem in academic prediction.Version of paper presented at symposium, Large Scale Academic Prediction Systems, American Psychological Association, New York, September 1966.