Optimal adaptive Bayesian design in choice experiments: performance for the population versus individuals

Peter Gibbard () and Kelson Sadlier
Additional contact information
Peter Gibbard: University of Otago
Kelson Sadlier: University of Otago

Marketing Letters, 2025, vol. 36, issue 3, No 16, 547-559

Abstract: Abstract The literature on conjoint analysis has examined the performance of adaptive designs of choice experiments, comparing them to simpler static designs. In assessing the “estimation accuracy” of designs, however, this literature has focussed on estimators of individual parameters rather than population parameters. In contrast, our paper assesses not only estimation accuracy for individual parameters but also for population parameters. In particular, we compare the performance of an optimal adaptive Bayesian design to a simple, crude design in which questions are selected randomly. We find that, for the population parameters, the estimation accuracy of the adaptive Bayesian design is uniformly worse than the random design. More generally, we conclude that this adaptive Bayesian design is more suitable when the researcher’s focus is on the specific individuals in the sample. When, however, the focus is on the population, this adaptive Bayesian design does not have a clear advantage over a random design.

Keywords: Optimal design; Adaptive design; Bayesian; Conjoint analysis (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11002-025-09768-4 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:mktlet:v:36:y:2025:i:3:d:10.1007_s11002-025-09768-4

Ordering information: This journal article can be ordered from
http://www.springer. ... etailsPage=societies

DOI: 10.1007/s11002-025-09768-4

Access Statistics for this article

Marketing Letters is currently edited by Joel Steckel and Peter Golder

More articles in Marketing Letters from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().