An improved two-stage variance balance approach for constructing partial profile designs for discrete choice experiments

Roselinde Kessels*, Bradley Jones, Peter Goos

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Web of Science)

Abstract

In many discrete choice experiments set up for product innovation, the number of attributes is large, which results in a substantial cognitive burden for the respondents. To reduce the cognitive burden in such cases, Green suggested in the early '70s the use of partial profiles that vary only the levels of a subset of the attributes. In this paper, we present two new methods for constructing Bayesian D‐optimal partial profile designs for estimating main‐effects models. They involve alternative generalizations of Green's approach that makes use of balanced incomplete block designs and take into account the fact that attributes may have differing numbers of levels. We refer to our methods as variance balance I and II because they vary an attribute with a larger number of levels more often than an attribute with fewer levels to stabilize the variances of the individual part‐worth estimates. The two variance balance methods differ in the way attributes with differing numbers of levels are weighted. Both methods provide statistically more efficient partial profile designs for differing numbers of attribute levels than another generalization of Green's approach that does not weight the attributes. This method is called attribute balance. We show results from an actual experiment in software development demonstrating the usefulness of our methods. Copyright (c) 2014 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)626-648
Number of pages23
JournalApplied Stochastic Models in Business and Industry
Volume31
Issue number5
DOIs
Publication statusPublished - 2015
Externally publishedYes

Keywords

  • discrete choice experiments
  • Bayesian
  • D-optimal design
  • partial profiles
  • lexicographic choice behavior
  • variance balance I and II
  • attribute balance

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