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Optimal Test Length for Maximum Absolute Prediction

Published online by Cambridge University Press:  01 January 2025

Paul Horst  and
Charlotte MacEwan
Paul Horst
Affiliation:
University of Washington
Charlotte MacEwan
Affiliation:
University of Washington
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Abstract

The concepts of multiple differential prediction and multiple absolute prediction are developed in earlier papers (2, 3). The problem of determining the optimal distribution of testing time for multiple differential prediction has been previously considered (4). This paper develops an analogous procedure for multiple absolute prediction. A numerical example illustrating the procedure is presented. The mathematical rationale underlying the procedure is given.

Information

Type
Original Paper
Information
Psychometrika , Volume 21 , Issue 2 , June 1956 , pp. 111 - 124
Copyright
Copyright © 1956 The Psychometric Society

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Footnotes

*

This research was carried out under Contract Nonr-477(08) between the University of Washington and the Office of Naval Research. The computations were carried out by Robert Dear and Donald Mills. Much credit is due the typist, Elizabeth Cross. Supervision of both computational and editorial activities was provided by William Clemans. To each of these able contributors we are deeply grateful.

References

Horst, P. Determination of optimal test length to maximize the multiple correlation. Psychometrika, 1949, 14, 79–88CrossRefGoogle ScholarPubMed
Horst, P. A technique for the development of a differential prediction battery. Psychol. Monogr., 1954, 68, No. 9 (Whole No. 380).CrossRefGoogle Scholar
Horst, P. A technique for the development of a multiple absolute prediction battery. Psychol. Monogr., 1955, 69, No. 5 (Whole No. 390).CrossRefGoogle Scholar
Horst, P. Optimal test length for maximum differential prediction. Psychometrika, 1956, 21, 51–66CrossRefGoogle Scholar
Horst, P. Servant of the human sciences, Chapt. 21, Section 7. Unpublished manuscript. Division of Counseling and Testing Services, University of Washington.Google Scholar