An integrated algorithm for the optimal design of stated choice experiments with partial profiles

Daniel Palhazi Cuervo*, Roselinde Kessels, Peter Goos, Kenneth Sorensen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Web of Science)

Abstract

Stated choice experiments are conducted to identify the attributes that drive people’s preferences when choosing between competing options. They are widely used in transportation in order to support the decision making of companies and governmental authorities. A large number of attributes might increase the complexity of the choice task in a choice experiment, and have a detrimental effect on the quality of the results obtained. In order to reduce the cognitive effort required by the experiment, researchers may resort to experimental designs where the levels of some attributes are held constant within a choice situation. These designs are called partial profile designs. In this paper, we propose an integrated algorithm for the generation of D-optimal designs for stated choice experiments with partial profiles. This algorithm optimizes the set of constant attributes and the levels of the varying attributes simultaneously. An extensive computational experiment shows that the designs produced by the integrated algorithm outperform those obtained by existing algorithms, and match the optimal designs that have been analytically derived for a number of benchmark instances. Additionally, we evaluate the performance of the algorithm under varying experimental conditions and study the structure of the designs generated. We also revisit two stated choice experiments in transportation, and describe how the integrated algorithm could help to improve their designs.
Original languageEnglish
Pages (from-to)648-669
Number of pages22
JournalTransportation Research Part B-Methodological
Volume93
DOIs
Publication statusPublished - Nov 2016
Externally publishedYes

Keywords

  • Stated choice experiments
  • Multinomial logit model
  • Partial profiles
  • (Bayesian) D-optimality
  • Utility-neutral designs
  • Coordinate-exchange algorithm

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