On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood

Psychometrika (2017)

21 Pages Posted: 7 Feb 2018

Date Written: June 12, 2017

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 Testing, Conjoint Measurement, Approximate Bayesian Computation

Suggested Citation

Karabatsos, George, On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood (June 12, 2017). Psychometrika (2017). Available at SSRN: https://ssrn.com/abstract=3102496

George Karabatsos (Contact Author)

University of Illinois at Chicago ( email )

1040 W Harrison St
Chicago, IL 60607
United States

HOME PAGE: http://georgek.people.uic.edu/

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