A Probabilistic Multidimensional Scaling Vector Model

Applied Psychological Measurement vol. 10 no. 1, pp. 79-98, 1986

21 Pages Posted: 25 May 2016

See all articles by Wayne S. DeSarbo

Wayne S. DeSarbo

Pennsylvania State University

Richard L. Oliver

Vanderbilt University - Marketing

Geert De Soete

Ghent University

Date Written: March 1986

Abstract

This article presents the development of a new stochastic multidimensional scaling (MDS) method, which operates on paired comparisons data and renders a spatial representation of subjects and stimuli. 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 respective information as to the utility (or whatever latent construct is under investigation) of the stimuli to the subjects. The psychometric literature concerning related MDS methods that also operate on paired comparisons data is reviewed, and a technical description of the new method is provided. A small monte carlo analysis performed on synthetic data with the new method is also presented. To illustrate the versatility of the model, an application measuring consumer satisfaction and investigating the impact of hypothesized determinants, using one of the optional re parameterized models, is described. Future areas of further research are identified.

Suggested Citation

DeSarbo, Wayne S. and Oliver, Richard L. and De Soete, Geert, A Probabilistic Multidimensional Scaling Vector Model (March 1986). Applied Psychological Measurement vol. 10 no. 1, pp. 79-98, 1986, Available at SSRN: https://ssrn.com/abstract=2783484

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Richard L. Oliver

Vanderbilt University - Marketing ( email )

Nashville, TN 37203
United States

Geert De Soete

Ghent University ( email )

Coupure Links 653
Gent, 9000
Belgium

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