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Developing Prediction Weights by Matching Battery Factorings

Published online by Cambridge University Press:  01 January 2025

Clifford E. Lunneborg
Clifford E. Lunneborg*
Affiliation:
University of Washington
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Abstract

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.

Information

Type
Original Paper
Information
Psychometrika , Volume 32 , Issue 2 , June 1967 , pp. 133 - 141
Copyright
Copyright © 1967 The Psychometric Society

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Footnotes

*

Version of paper presented at symposium, “Large Scale Academic Prediction Systems,” American Psychological Association, New York, September 1966.

References

Burket, G. R. A study of reduced rank models for multiple prediction. Psychometric Monographs, 1964, No. 12.Google Scholar
Horst, P. Factor analysis of data matrices, New York: Holt, Rinehart and Winston, 1965.Google Scholar
Langen, T. D. F. An investigation of additional predictor and criterion variables for the Washington Pre-College Testing Program with subdivision by sex and extent of achievement, Seattle: Univ. of Washington, 1965.Google Scholar