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

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  • Roselinde Kessels
  • Bradley Jones
  • Peter Goos

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 © 2014 John Wiley & Sons, Ltd.

Suggested Citation

  • Roselinde Kessels & Bradley Jones & Peter Goos, 2015. "An improved two‐stage variance balance approach for constructing partial profile designs for discrete choice experiments," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(5), pages 626-648, September.
  • Handle: RePEc:wly:apsmbi:v:31:y:2015:i:5:p:626-648
    DOI: 10.1002/asmb.2065
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    Citations

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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. Luyten, Jeroen & Beutels, Philippe & Vandermeulen, Corinne & Kessels, Roselinde, 2022. "Social preferences for adopting new vaccines in the national immunization program: A discrete choice experiment," Social Science & Medicine, Elsevier, vol. 303(C).
    3. Bansal, Prateek & Kessels, Roselinde & Krueger, Rico & Graham, Daniel J., 2022. "Preferences for using the London Underground during the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 45-60.
    4. Meyerhoff, Jürgen & Oehlmann, Malte, 2023. "The performance of full versus partial profile choice set designs in environmental valuation," Ecological Economics, Elsevier, vol. 204(PA).
    5. 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.
    6. 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.
    7. Luyten, Jeroen & Kessels, Roselinde & Atkins, Katherine E. & Jit, Mark & van Hoek, Albert Jan, 2019. "Quantifying the public's view on social value judgments in vaccine decision-making: A discrete choice experiment," Social Science & Medicine, Elsevier, vol. 228(C), pages 181-193.
    8. Kotzab, Herbert & Yumurtacı Hüseyinoğlu, Işık Özge & Şen, Irmak & Mena, Carlos, 2024. "Exploring home delivery service attributes: Sustainability versus delivery expectations during the COVID-19 pandemic," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).

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