eCommons

 

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

Other Titles

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.

Journal / Series

Volume & Issue

Description

Sponsorship

Date Issued

1991-08-01

Publisher

Keywords

cluster analysis; categorization; sorting tasks; maximum likelihood estimation

Location

Effective Date

Expiration Date

Sector

Employer

Union

Union Local

NAICS

Number of Workers

Committee Chair

Committee Co-Chair

Committee Member

Degree Discipline

Degree Name

Degree Level

Related Version

Related To

Related Part

Based on Related Item

Has Other Format(s)

Part of Related Item

Related To

Related Publication(s)

Link(s) to Related Publication(s)

References

Link(s) to Reference(s)

Previously Published As

Government Document

ISBN

ISMN

ISSN

Other Identifiers

Rights

Required 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.

Rights URI

Types

article

Accessibility Feature

Accessibility Hazard

Accessibility Summary

Link(s) to Catalog Record