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On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood

Published online by Cambridge University Press:  01 January 2025

George Karabatsos
George Karabatsos*
Affiliation:
University of Illinois-Chicago
*
Correspondence should be made to George Karabatsos, University of Illinois-Chicago, Chicago, IL USA. Email: [email protected]
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Abstract

This article introduces a Bayesian method for testing the axioms of additive conjoint measurement. The method is based on an importance sampling algorithm that performs likelihood-free, approximate Bayesian inference using a synthetic likelihood to overcome the analytical intractability of this testing problem. This new method improves upon previous methods because it provides an omnibus test of the entire hierarchy of cancellation axioms, beyond double cancellation. It does so while accounting for the posterior uncertainty that is inherent in the empirical orderings that are implied by these axioms, together. The new method is illustrated through a test of the cancellation axioms on a classic survey data set, and through the analysis of simulated data.

Keywords

axiom testingconjoint measurementapproximate Bayesian computation
Type
Original Paper
Information
Psychometrika , Volume 83 , Issue 2 , June 2018 , pp. 321 - 332
Copyright
Copyright © 2017 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (doi:10.1007/s11336-017-9581-x) contains supplementary material, which is available to authorized users.

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