ERIC Number: ED579807
Record Type: Non-Journal
Publication Date: 2017-Jun-6
Pages: 21
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-
EISSN: N/A
Available Date: N/A
On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood
Karabatsos, George
Grantee Submission
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. [At time of submission to ERIC this article was in press with "Psychometrika."]
Publication Type: Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: SES0242030; SES1156372
Author Affiliations: N/A