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Linear Programming Techniques for Multidimensional Analysis of Preferences

Published online by Cambridge University Press:  01 January 2025

V. Srinivasan  and
Allan D. Shocker
V. Srinivasan
Affiliation:
The University of Rochester
Allan D. Shocker
Affiliation:
University of Pittsburgh
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Abstract

This paper offers a new methodology for analyzing individual differences in preference judgments with regard to a set of stimuli prespecified in a multidimensional attribute space. The individual is modelled as possessing an “ideal point” denoting his most preferred stimulus location in this space and a set of weights which reveal the relative saliences of the attributes. He prefers those stimuli which are “closer” to his ideal point (in terms of a weighted Euclidean distance measure). A linear programming model is proposed for “external analysis” i.e., estimation of the coordinates of his ideal point and the weights (involved in the Euclidean distance measure) by analyzing his paired comparison preference judgments on a set of stimuli, prespecified by their coordinate locations in the multidimensional space. A measure of “poorness of fit” is developed and the linear programming model minimizes this measure over all possible solutions. The approach is fully nonmetric, extremely flexible, and uses paired comparison judgments directly. The weights can either be constrained nonnegative or left unconstrained. Generalizations of the model to consider ordinal or interval preference data and to allow an orthogonal transformation of the attribute space are discussed. The methodology is extended to perform “internal analysis,”i.e., to determine the stimuli locations in addition to weights and ideal points by analyzing the preference judgments of all subjects simultaneously. Computational results show that the methodology for external analysis is “unbiased”—i.e., on an average it recovers the “true” ideal point and weights. These studies also indicate that the technique performs satisfactorily even when about 20 percent of the paired comparison judgments are incorrectly specified.

Information

Type
Original Paper
Information
Psychometrika , Volume 38 , Issue 3 , September 1973 , pp. 337 - 369
Copyright
Copyright © 1973 The Psychometric Society

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Footnotes

*

The authors wish to express their deep appreciation to J. Douglas Carroll and Joseph B. Kruskal for their most valuable comments on an earlier version of this paper.

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