A new clustering methodology for the analysis of sorted or categorized stimuli |
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Authors: | Wayne S Desarbo Kamel Jedidi Michael D Johnson |
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Institution: | (1) School of Business Administration, The University of Michigan, 48109-1234 Ann Arbor, MI, USA;(2) Columbia University, Manhatta, USA;(3) University of Michigan, Ann Arbor, USA |
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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.
Wayne S. DeSarbo is the S. S. Kresge Distinguished Professor of Marketing and Statistics, and Michael D. Johnson is Associate
Professor of Marketing, both at the University of Michigan's School of Business Administration. Kamel Jedidi is Assistant
Professor of Marketing at Columbia University's Graduate School of Business. The authors gratefully acknowledge DuPont Incorporated
for providing financial support for this research. |
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Keywords: | Cluster Analysis Categorization Sorting Tasks Maximum Likelihood Estimation |
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