The usefulness of Bayesian optimal designs for discrete choice experiments

Roselinde Kessels*, Bradley Jones, Peter Goos, Martina Vandebroek

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated choice experiments or conjoint choice experiments, has gained much attention, stimulating the development of Bayesian choice design algorithms. Characteristic for the Bayesian design strategy is that it incorporates the available information about people's preferences for various product attributes in the choice design. This is in contrast with the linear design methodology, which is also used in discrete choice design and which depends for any claims of optimality on the unrealistic assumption that people have no preference for any of the attribute levels. Although linear design principles have often been used to construct discrete choice experiments, we show using an extensive case study that the resulting utility‐neutral optimal designs are not competitive with Bayesian optimal designs for estimation purposes. Copyright (c) 2011 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)173-188
Number of pages16
JournalApplied Stochastic Models in Business and Industry
Issue number3
Publication statusPublished - 2011
Externally publishedYes


  • choice experiments
  • stated choice data
  • Bayesian design
  • utility-neutral or linear design
  • orthogonal design
  • D-optimality


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