CATSCALE: A stochastic multidimensional scaling methodology for the spatial analysis of sorting data and the study of stimulus categorization

Wayne S. DeSarbo, Robert Libby, Kamel Jedidi

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Sorting tasks have provided researchers with valuable alternatives to traditional paired-comparison similarity judgments. They are particularly well-suited to studies involving large stimulus sets where exhaustive paired-comparison judgments become infeasible, especially in psychological studies investigating stimulus categorization. This paper presents a new stochastic multidimensional scaling procedure called CATSCALE for the analysis of three-way sorting data (as typically collected in categorization studies). We briefly present a review of the role of sorting tasks, especially in categorization studies, as well as a description of several traditional modes of analysis. The CATSCALE model and maximum likelihood based estimation procedure are described. An application of CATSCALE is presented with respect to a behavioral accounting study examining auditor's categorization processes with respect to various types of errors found in typical financial statements.

Original languageEnglish (US)
Pages (from-to)165-184
Number of pages20
JournalComputational Statistics and Data Analysis
Issue number1
Publication statusPublished - Aug 1994


All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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