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

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.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

Understanding consumer usage of product magnitudes through sorting tasks

“…However, a sorting task has several characteristics that make it a very suitable alternative for examining how consumers think about product attributes, and specifically magnitude estimation. Foremost, a sorting task is a natural way of revealing a respondent's internal perceptions or knowledge base (DeSarbo, Jedidi, & Johnson, 1991). A sorting task is openended, and, unlike category scaling, does not impose a certain number of categories on the respondents.…”