A nonspatial methodology for the analysis of two-way proximity data incorporating the distance-density hypothesis

Wayne S. DeSarbo, Ajay K. Manrai, Raymond R. Burke

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

This paper presents a nonspatial operationalization of the Krumhansl (1978, 1982) distancedensity model of similarity. This model assumes that the similarity between two objects i and j is a function of both the interpoint distance between i and j and the density of other stimulus points in the regions surrounding i and j. We review this conceptual model and associated empirical evidence for such a specification. A nonspatial, tree-fitting methodology is described which is sufficiently flexible to fit a number of competing hypotheses of similarity formation. A sequential, unconstrained minimization algorithm is technically presented together with various program options. Three applications are provided which demonstrate the flexibility of the methodology. Finally, extensions to spatial models, three-way analyses, and hybrid models are discussed.

Original languageEnglish (US)
Pages (from-to)229-253
Number of pages25
JournalPsychometrika
Volume55
Issue number2
DOIs
StatePublished - Jun 1990

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

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