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

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  • CUERVO, Daniel Palhazi
  • KESSELS, Roselinde
  • GOOS, Peter
  • SÖRENSEN, Kenneth

Abstract

Stated choice experiments are conducted to identify the attributes that drive people's preferences when choosing between competing options of products or services. 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. We also evaluate the performance of the algorithm under varying experimental conditions and study the structure of the designs generated.

Suggested Citation

  • CUERVO, Daniel Palhazi & KESSELS, Roselinde & GOOS, Peter & SÖRENSEN, Kenneth, 2015. "An integrated algorithm for the optimal design of stated choice experiments with partial profiles," Working Papers 2015004, University of Antwerp, Faculty of Business and Economics.
  • Handle: RePEc:ant:wpaper:2015004
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    References listed on IDEAS

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    Cited by:

    1. Großmann, Heiko, 2019. "A practical approach to designing partial-profile choice experiments with two alternatives for estimating main effects and interactions of many two-level attributes," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    2. Verelst, Frederik & Willem, Lander & Kessels, Roselinde & Beutels, Philippe, 2018. "Individual decisions to vaccinate one's child or oneself: A discrete choice experiment rejecting free-riding motives," Social Science & Medicine, Elsevier, vol. 207(C), pages 106-116.
    3. Kessels, Roselinde, 2016. "Homogeneous versus heterogeneous designs for stated choice experiments: Ain't homogeneous designs all bad?," Journal of choice modelling, Elsevier, vol. 21(C), pages 2-9.
    4. Van Acker, Veronique & Kessels, Roselinde & Palhazi Cuervo, Daniel & Lannoo, Steven & Witlox, Frank, 2020. "Preferences for long-distance coach transport: Evidence from a discrete choice experiment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 759-779.

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    Keywords

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

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