Hierarchical Bayesian conjoint models incorporating measurement uncertainty

John C. Liechty, Duncan K.H. Fong, Eelko K.R.E. Huizingh, Arnaud De Bruyn

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The authors explore situations where consumers supplement their judgments with a measurement of uncertainty about their own preferences, either implicitly or explicitly, and develop two sets of hierarchical Bayesian conjoint models incorporating such measurements. The first set of models uses the relative location of a rating to determine the importance or weight given to the rating, in a regression setting. The second set uses interval judgment as a dependent variable in a regression setting. After specifying the models, the authors perform a theoretical comparison with a basic Bayesian regression model. They show that, under different conditions, the proposed models will yield more precise individual-level partworth estimates. Two simulated data examples and data from a conjoint study are used to illustrate the gains that could be obtained from modeling uncertainty. In the empirical application, the authors show that model fit improves when ratings for items that respondents do not like are given more weight compared to ratings for items that they do like.

Original languageEnglish (US)
Pages (from-to)141-155
Number of pages15
JournalMarketing Letters
Issue number2
StatePublished - Jun 2008

All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Economics and Econometrics
  • Marketing


Dive into the research topics of 'Hierarchical Bayesian conjoint models incorporating measurement uncertainty'. Together they form a unique fingerprint.

Cite this