Axiomatic and numerical conjoint measurement: An evaluation of diagnostic efficacy

Douglas R. Emery, F. Hutton Barron

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Synthetic data are used to examine how well axiomatic and numerical conjoint measurement methods, individually and comparatively, recover simple polynomial generators in three dimensions. The study illustrates extensions of numerical conjoint measurement (NCM) to identify and model distributive and dual-distributive, in addition to the usual additive, data structures. It was found that while minimum STRESS was the criterion of fit, another statistic, predictive capability, provided a better diagnosis of the known generating model. That NCM methods were able to better identify generating models conflicts with Krantz and Tversky's assertion that, in general, the direct axiom tests provide a more powerful diagnostic test between alternative composition rules than does evaluation of numerical correspondence. For all methods, dual-distributive models are most difficult to recover, while consistent with past studies, the additive model is the most robust of the fitted models.

Original languageEnglish (US)
Pages (from-to)195-210
Number of pages16
Issue number2
StatePublished - Jun 1979
Externally publishedYes


  • conjoint measurement
  • model diagnosis
  • ordinal data
  • scaling
  • simple polynomials

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics


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