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Journal of Statistical Theory and Practice

, Volume 11, Issue 2, pp 339–360 | Cite as

Minimum aberration designs for discrete choice experiments

  • Jessica Jaynes
  • Hongquan Xu
  • Weng Kee Wong
Article

Abstract

A discrete choice experiment (DCE) is a survey method that gives insight into individual preferences for particular attributes. Traditionally, methods for constructing DCEs focus on identifying the individual effect of each attribute (a main effect). However, an interaction effect between two attributes (a two-factor interaction) better represents real-life trade-offs, and provides us a better understanding of subjects’ competing preferences. In practice it is often unknown which two-factor interactions are significant. To address the uncertainty, we propose the use of minimum aberration blocked designs to construct DCEs. Such designs maximize the number of models with estimable two-factor interactions in a DCE with two-level attributes. We further extend the minimum aberration criteria to DCEs with mixed-level attributes and develop some general theoretical results.

Keywords

Aliasing blocked design estimation capacity fractional factorial design multinomial logit model orthogonal array 

AMS Subject Classification

62K15 62J15 62K10 

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Copyright information

© Grace Scientific Publishing, 20 Middlefield Ct, Greensboro, NC 27455 2017

Authors and Affiliations

  1. 1.Department of MathematicsCalifornia State University, FullertonFullertonUSA
  2. 2.Department of StatisticsUniversity of CaliforniaLos AngelesUSA
  3. 3.Department of BiostatisticsUniversity of CaliforniaLos AngelesUSA

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