Hostname: page-component-6bf8c574d5-vmclg Total loading time: 0 Render date: 2025-02-27T02:37:46.327Z Has data issue: false hasContentIssue false
  • English
  • Français

The Selection of Judges for Preference Testing

Published online by Cambridge University Press:  01 January 2025

R. Darrell Bock
R. Darrell Bock*
Affiliation:
University of Chicago
Get access Rights & Permissions [Opens in a new window]

Abstract

A scheme for choosing a few individuals whose preferences for given objects are most representative of those of a larger group of individuals is proposed. The method involves (a) quantifying the preferences of each individual so as to discriminate optimally among objects, (b) testing statistically whether or not a common preference continuum may be assumed for the quantified preferences, (c) constructing a linear estimator of values for the objects on this continuum, if it may be assumed, and (d) selecting as judges the least number of individuals whose quantified preferences, when used with this estimator, determine values for the objects with acceptable accuracy. A numerical example based on food preferences is presented.

Type
Original Paper
Information
Psychometrika , Volume 21 , Issue 4 , December 1956 , pp. 349 - 366
Copyright
Copyright © 1956 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

Preparation of this paper has been supported in part by the Quartermaster Food and Container Institute for the Armed Forces. Views or conclusions expressed herein are those of the author and do not necessarily reflect the views or indorsement of the Department of Defense.

References

Bartlett, M. S. Further aspects of the theory of multiple regression. Proc. Camb. phil. Soc., 1938, 34, 33–40CrossRefGoogle Scholar
Bartlett, M. S. Multivariate analysis. J. roy. statist. Soc., Suppl., 1947, 9, 176–190CrossRefGoogle Scholar
Bartlett, M. S. Internal and external factor analysis. Brit. J. Psychol., Statist. Sect., 1948, 1, 73–81CrossRefGoogle Scholar
Bartlett, M. S. The goodness of fit of a single hypothetical discriminant function in the case of several groups. Ann. Eugen., 1951, 16, 199–214CrossRefGoogle ScholarPubMed
Fisher, R. A. The precision of discriminant functions. Ann. Eugen., 1940, 10, 422–429CrossRefGoogle Scholar
Fisher, R. A. Statistical methods for research workers, New York: Hafner, 1948Google Scholar
Guttman, L. et al. The quantification of a class of attributes: a theory and method of scale construction. In Horst, P. et al (Eds.), The prediction of personal adjustment (pp. 319–348). New York: Social Science Research Council, 1941Google Scholar
Guttman, L. An approach for quantifying paired comparisons and rank order. Ann. math. Statist., 1946, 17, 144–163CrossRefGoogle Scholar
Guttman, L. et al. Components of scale analysis. In Stouffer, et al (Eds.), Measurement and prediction (pp. 312–361). Princeton: Princeton Univ. Press, 1950Google Scholar
Hartree, D. R. Numerical analysis, Oxford: Clarendon Press, 1952Google Scholar
Holzinger, K. J. and Harman, H. H. Factor analysis, Chicago: Univ. Chicago Press, 1941Google Scholar
Johnson, P. O. The quantification of data in discriminant analysis. J. A. S. A., 1950, 45, 65–76CrossRefGoogle Scholar
Kelley, T. L. Interpretation of educational measurements, Yonkers-on-Hudson: World Book, 1927Google Scholar
Kendall, M. J. The advanced theory of statistics, London: Griffin, 1948Google Scholar
Marriott, F. H. C. Tests of significance in canonical analysis. Biometrika, 1952, 39, 58–64CrossRefGoogle Scholar
Maung, Khint. Discriminant analysis of Tocher's eye-color data for Scottish school children. Ann. Eugen., 1941, 11, 64–76CrossRefGoogle Scholar
Overman, A. and Li, J. C. R. Dependability of food judges as indicated by analysis of scores of food testing panel. Food Res., 1948, 13, 441–449CrossRefGoogle ScholarPubMed
Rao, C. R. Advanced statistical methods in biometric research, New York: Wiley, 1952Google Scholar
Williams, E. J. Use of scores for the analysis of association in contingency tables. Biometrika, 1952, 39, 274–289CrossRefGoogle Scholar