The authors use conjoint analysis to provide interval-level estimates of part-worths allowing tradeoffs among attribute levels to be examined. Researchers often possess prior information about the part-worths, such as the order and range restrictions of product attribute levels. It is known, for example, that consumers would rather pay less for a specific product given that all other product attribute levels are unchanged. The authors present a Bayesian approach to incorporate prior ordinal information about these part-worths into the analysis of conjoint studies. Their method results in parameter estimates with greater face validity and predictive performance than estimates that do not utilize prior information or those that use traditional methods such as LINMAP. Unlike existing methods, the authors’ methods apply to both rating and choice-based conjoint studies.
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