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Maximizing the Coefficient of Generalizability under the Constraint of Limited Resources

Published online by Cambridge University Press:  01 January 2025

P. F. Sanders ,
T. J. J. M. Theunissen  and
S. M. Baas
P. F. Sanders*
Affiliation:
National Institute for Educational Measurement (CITO)
T. J. J. M. Theunissen
Affiliation:
National Institute for Educational Measurement (CITO)
S. M. Baas
Affiliation:
University of Twente
*
Requests for reprints should be sent to P. F. Sanders, Cito, PO Box 1034, 6801 MG Arnhem, THE NETHERLANDS.
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Abstract

A procedure for maximizing the coefficient of generalizability under the constraint of limited resources is presented. The procedure uses optimization techniques that offer an investigator or test constructor the possibility of employing practical constraints. The procedure is illustrated for the two-facet random-model crossed design.

Keywords

generalizability theoryoptimization of decision studiesoptimization techniques
Type
Original Paper
Information
Psychometrika , Volume 56 , Issue 1 , March 1991 , pp. 87 - 96
Copyright
Copyright © 1991 The Psychometric Society

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References

Brennan, R. L. (1983). Elements of generalizability theory, Iowa City: ACT Publications.Google Scholar
Cardinet, J., & Tourneur, Y. (1985). Assurer la mesure [Adequate Measurement], Bern: Peter Lang.Google Scholar
Crick, J. E., & Brennan, R. L. (1982). GENOVA: a generalized analysis of variance system [FORTRAN IV computer program and manual], Iowa City: ACT.Google Scholar
Cronbach, L. J., Gleser, G. C., Nanda, H., & Rajaratnam, N. (1972). The dependability of behavioral measurements, New York: Wiley.Google Scholar
Papadimitriou, Ch. H., & Steiglitz, K. (1982). Combinatorial optimization: Algorithms and complexity, Englewood Cliffs: Prentice-Hall.Google Scholar
Sanders, P. F., Theunissen, T. J. J. M., & Baas, S. (1989). Minimizing the number of observations: A generalization of the Spearman-Brown formula. Psychometrika, 54, 587–598.CrossRefGoogle Scholar
Theunissen, T. J. J. M. (1985). Binary programming and test design. Psychometrika, 50, 411–420.CrossRefGoogle Scholar
van der Linden, W. J., & Boekkooi-Timminga, E. (1989). A maximin model for test design with practical constraints. Psychometrika, 54, 237–247.CrossRefGoogle Scholar
van der Linden, W. J., & Adema, J. J. (1988). Algorithmic test design using classical item parameters, Enschede, The Netherlands: University of Twente, Department of Education.Google Scholar
Woodward, J. A., & Joe, G. W. (1973). Maximizing the coefficient of generalizability in multi-facet decision studies. Psychometrika, 38, 173–181.CrossRefGoogle Scholar