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Axiomatic and Numerical Conjoint Measurement: An Evaluation of Diagnostic Efficacy

Published online by Cambridge University Press:  01 January 2025

Douglas R. Emery  and
F. Hutton Barron
Douglas R. Emery
Affiliation:
The University of Calgary
F. Hutton Barron*
Affiliation:
The University of Kansas
*
Requests for reprints should be sent to F. H. Barron, School of Business, University of Kansas, Lawrence, Kansas, 66045.
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Abstract

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.

Keywords

conjoint measurementscalingsimple polynomialsmodel diagnosisordinal data
Type
Original Paper
Information
Psychometrika , Volume 44 , Issue 2 , June 1979 , pp. 195 - 210
Copyright
Copyright © 1979 The Psychometric Society

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Footnotes

Douglas Emery is now at the Krannert Graduate School of Management, Purdue University, West Lafayette, IN, on leave from the University of Calgary.

References

Reference Note

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