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dc.contributor.authorDesarbo, Wayne S.
dc.contributor.authorJedidi, Kamel
dc.contributor.authorJohnson, Michael D.
dc.description.abstractThis 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.
dc.rightsRequired Publisher Statement: © Springer. Final version published as: Desarbo, W. S., Jedidi, K., & Johnson, M. D. (1991). A new clustering methodology for the analysis of sorted or categorized stimuli. Marketing Letters, 2(3), 267–279. Reprinted with permission. All rights reserved.
dc.subjectcluster analysis
dc.subjectsorting tasks
dc.subjectmaximum likelihood estimation
dc.titleA New Clustering Methodology for the Analysis of Sorted or Categorized Stimuli
dc.description.legacydownloadsJohnson47_A_New_Clustering.pdf: 90 downloads, before Aug. 1, 2020.
local.authorAffiliationDesarbo, Wayne S.: University of Michigan-Ann Arbor
local.authorAffiliationJedidi, Kamel: Columbia University
local.authorAffiliationJohnson, Michael D.: Cornell University School of Hotel Administration

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