Abstract

No study has compared the relative effectiveness of finite mixture and hierarchical Bayes conjoint analysis models in terms of fit, prediction, and parameter recovery. To conduct such a comparison, the authors employ the simulation methodology proposed by Vriens, Wedel, and Wilms with some modification. The authors estimate traditional individual-level conjoint models as well. The authors show that FM and HB models are equally effective in recovering individual-level parameters and predicting ratings of holdout profiles. Two surprising findings are that: 1. HB performs well even when partworths come from a mixture of distributions and, 2. FM produces good parameter estimates, even at the individual level. The authors show that both models are quite robust to violations of underlying assumptions and that traditional individual-level models overfit the data.

View research
Download PDF

Citation

Andrews, Rick, Asim Ansari, and Imran Currim. "Hierachical Bayes Versus Finite Mixture Conjoint Analysis Models: A Comparision of Fit, Prediction, and Part-Worth Recovery." Journal of Marketing Research 39, no. 1 (February 2002): 87-98.

Each author name for a Columbia Business School faculty member is linked to a faculty research page, which lists additional publications by that faculty member.

Each topic is linked to an index of publications on that topic.