Probabilistic Multidimensional Scaling Models for Analyzing Consumer Choice

Communication & Cognition Vol. 20, No. 1, pp. 93-116

13 Pages Posted: 26 May 2016

See all articles by Wayne S. DeSarbo

Wayne S. DeSarbo

Pennsylvania State University

Geert De Soete

Ghent University-Universiteit Gent

Kamel Jedidi

Columbia Business School - Marketing

Date Written: 1987

Abstract

We review the development of two new stochastic multidimensional scaling (MDS) methodologies that operate on paired comparisons choice data and render a spatial representation of subjects and stimuli. In the probabilistic vector MDS model, subjects are represented as vec­tors and stimuli as points in a T-dimensional space, where the scalar products or projections of the stimulus points onto the subject vectors provide information about the utility of the stimuli to the subjects.

In the probabilistic unfolding MDS model, subjects are represented as ideal points and stimuli as points in a T-dimensional space, where the Euclidean distance between the stimulus points and the subject ideal points provides information as to the respective utility of the stimuli to the subjects. To illustrate the versatility of the two models, a market­ing application measuring consumer choice for fourteen actual brands of over-the-counter analgesics, utilizing optional reparameterizations, is described. Finally, other applications are identified.

Suggested Citation

DeSarbo, Wayne S. and De Soete, Geert and Jedidi, Kamel, Probabilistic Multidimensional Scaling Models for Analyzing Consumer Choice (1987). Communication & Cognition Vol. 20, No. 1, pp. 93-116. Available at SSRN: https://ssrn.com/abstract=2783986

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Geert De Soete

Ghent University-Universiteit Gent ( email )

Coupure Links 653
Gent, 9000
Belgium

Kamel Jedidi

Columbia Business School - Marketing ( email )

New York, NY 10027
United States

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