Mixed tree and spatial representations of dissimilarity judgments

Wedel, M. & Bijmolt, THA., 2000, In : Journal of Classification. 17, 2, p. 243-271 29 p.

Research output: Contribution to journalArticleAcademicpeer-review

Whereas much previous research, focusing on the comparative fit at the aggregate level, has shown that either tree or spatial representations of dissimilarity judgments may be appropriate, we investigate whether there exist classes of subjects differing in the extent to which dissimilarity judgments are better represented by additive tree or spatial multidimensional scaling models. We develop a mixture model for the analysis of dissimilarity data that entails both representation and measurement model components. The latter involves distributional assumptions on the measurement error and enables estimation by maximum likelihood. The former component allows dissimilarity judgments to be represented either by an additive tree structure or by a spatial configuration, or a mixture of both. To investigate the appropriateness of additive tree versus spatial representations, the model is applied to twenty empirical data sets. We compare the fit of our model with that of aggregate additive tree and spatial models, as well as with mixtures of additive trees and mixtures of spatial configurations, respectively. We formulate some empirical generalizations on the relative importance of tree versus spatial structures in representing dissimilarity judgments.

Original languageEnglish
Pages (from-to)243-271
Number of pages29
JournalJournal of Classification
Issue number2
Publication statusPublished - 2000


  • multidimensional scaling, additive trees, mixture models, dissimilarity judgments, INDIVIDUAL-DIFFERENCES, PROXIMITY MATRICES, SIMILARITY, ALGORITHM, MODELS, DISTANCE, INDCLUS

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