, Volume 32, Issue 2, pp 133–141 | Cite as

Developing prediction weights by matching battery factorings

  • Clifford E. Lunneborg


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.


Shrinkage Public Policy Statistical Theory Sample Error Regression Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Burket, G. R. A study of reduced rank models for multiple prediction.Psychometric Monographs, 1964, No. 12.Google Scholar
  2. Horst, P.Factor analysis of data matrices. New York: Holt, Rinehart and Winston, 1965.Google Scholar
  3. 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. Unpublished doctoral dissertation, Univ. of Washington, Seattle, 1965.Google Scholar

Copyright information

© Psychometric Society 1967

Authors and Affiliations

  • Clifford E. Lunneborg
    • 1
  1. 1.University of WashingtonUSA

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