A New Clustering Methodology for the Analysis of Sorted or Categorized Stimuli

Marketing Letters, Volume 2, Issue 3, pp 267-279

13 Pages Posted: 1 Jun 2016

See all articles by Wayne S. DeSarbo

Wayne S. DeSarbo

Pennsylvania State University

Kamel Jedidi

Columbia Business School - Marketing

Michael D. Johnson

Stephen M. Ross School of Business at the University of Michigan

Date Written: August 1991

Abstract

This paper introduces a new stochastic clustering methodology devised for the analysis of categorized or sorted data. The methodology reveals consumers' common category knowledge as well as individual differences in using this knowledge for classifying brands in a designated product class. A small study involving the categorization of 28 brands of U.S. automobiles is presented where the results of the proposed methodology are compared with those obtained from KMEANS clustering. Finally, directions for future research are discussed.

Keywords: Cluster Analysis, Categorization, Sorting Tasks, Maximum Likelihood Estimation

Suggested Citation

DeSarbo, Wayne S. and Jedidi, Kamel and Johnson, Michael David, A New Clustering Methodology for the Analysis of Sorted or Categorized Stimuli (August 1991). Marketing Letters, Volume 2, Issue 3, pp 267-279. Available at SSRN: https://ssrn.com/abstract=2787362

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Kamel Jedidi

Columbia Business School - Marketing ( email )

New York, NY 10027
United States

Michael David Johnson

Stephen M. Ross School of Business at the University of Michigan ( email )

701 Tappan Street
Ann Arbor, MI MI 48109-1234
United States

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