A stochastic multidimensional scaling procedure for the empirical determination of convex indifference curves for preference/choice analysis

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.A wide range of methods and procedures exists for the analysis of similarity relations among stimuli. These methods range from traditional spatial multidimensional scaling methods (Shepard 1962;Kruskal 1964) to network clustering methods (Johnson 1967;Shepard and Arabie 1979). The input to such procedures is typically some measure of similarity, whether obtained from paired comparison judgments, or derived from some similarity-based response (e.g., sorting data, classification errors, preference relations, or choice data). One such class of input proximity data is the sorting of stimuli (on the basis of their similarity) into homogeneous subsets or categories. Sorting tasks are employed in a wide range of basic and applied research contexts. When subjects face a. large number of stimuli (Rao and Katz 1971) or have a limited ability to respond (Horton and Markm~ 1980), a sorting task may be gainfully employed.Unfortunately, traditional methods may be ill-suited to this particular type of similarity-based response. Such methods may not capture the category perceptions that underlie the sorting task or the individual differences involved. Our goal is to describe a new clustering methodology designed specifically for the analysis

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A new clustering methodology for the analysis of sorted or categorized stimuli

DeSarbo

Jedidi

Johnson

1991

Market Lett

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A new clustering methodology for the analysis of sorted or categorized stimuli

DeSarbo

Jedidi²,

Johnson

1991

Market Lett

Self Cite

View full text Add to dashboard Cite

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Research for product positioning and design decisions: An integrative review

Kaul¹,

Rao²

1995

International Journal of Research in Marketing

193

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A stochastic multidimensional scaling procedure for the empirical determination of convex indifference curves for preference/choice analysis

Abstract: multidimensional scaling, paired comparisons data, maximum likelihood estimation, microeconomics, consumer psychology,

Cited by 3 publications

References 48 publications

A new clustering methodology for the analysis of sorted or categorized stimuli

A new clustering methodology for the analysis of sorted or categorized stimuli

A new clustering methodology for the analysis of sorted or categorized stimuli

Research for product positioning and design decisions: An integrative review

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