Hierarchical Bayes estimation for choice data represents one of the most successful new developments in our field. HB has proven robust for ratings-based conjoint, ACA, and full-profile CBC projects. Tests comparing HB to other methods of part worth estimation have generally favored HB. However, two anomalies specific to HB estimation have caused us some puzzlement and concern.
- The "omitted" level in effects coding for conjoint analysis results in overstatement of the variance, and in extreme cases (very sparse data and very many levels within an attribute) biased point estimates.
- HB was demonstrated to have problems in individual-level estimation for some partial-profile CBC data sets.
This paper shows that the problems above can be controlled or even solved by setting proper "priors" in HB. The anomalies therefore do not point to a weakness in HB methods, but simply illustrate that we were not defining the models properly in certain circumstances. HB researchers should be aware of the kinds of data sets that can challenge "generic" HB estimation under Sawtooth Software's default settings, and learn to manage these through more proper specification of the priors.