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Multiclus: A New Method for Simultaneously Performing Multidimensional Scaling and Cluster Analysis

Published online by Cambridge University Press:  01 January 2025

Wayne S. DeSarbo ,
Daniel J. Howard  and
Kamel Jedidi
Wayne S. DeSarbo*
Affiliation:
Departments of Marketing and Statistics, The University of Michigan
Daniel J. Howard
Affiliation:
Marketing Department, E. L. Cox School of Business, Southern Methodist University
Kamel Jedidi
Affiliation:
Marketing Department, Graduate School of Business, Columbia University
*
Reprint requests should be sent to Wayne S. DeSarbo, Marketing and Statistics Depts., School of Business, The University of Michigan, Ann Arbor, MI 48109.
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Abstract

This paper develops a maximum likelihood based method for simultaneously performing multidimensional scaling and cluster analysis on two-way dominance or profile data. This MULTICLUS procedure utilizes mixtures of multivariate conditional normal distributions to estimate a joint space of stimulus coordinates and K vectors, one for each cluster or group, in a T-dimensional space. The conditional mixture, maximum likelihood method is introduced together with an E-M algorithm for parameter estimation. A Monte Carlo analysis is presented to investigate the performance of the algorithm as a number of data, parameter, and error factors are experimentally manipulated. Finally, a consumer psychology application is discussed involving consumer expertise/experience with microcomputers.

Keywords

multidimensional scalingcluster analysismaximum likelihood estimationconsumer psychology
Type
Original Paper
Information
Psychometrika , Volume 56 , Issue 1 , March 1991 , pp. 121 - 136
Copyright
Copyright © 1991 The Psychometric Society

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Footnotes

We wish to thank the editor, associate editor, and three anonymous reviewers for their helpful comments on earlier versions of this manuscript.

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