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Efficient Conjoint Choice Designs in the Presence of Respondent Heterogeneity


Author Info

  • Jie Yu

    (Faculty of Business and Economics, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium)

  • Peter Goos

    (Faculty of Applied Economics, Universiteit Antwerpen, B-2000 Antwerpen, Belgium)

  • Martina Vandebroek

    (Faculty of Business and Economics and Leuven Statistics Research Centre, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium)


Random effects or mixed logit models are often used to model differences in consumer preferences. Data from choice experiments are needed to estimate the mean vector and the variances of the multivariate heterogeneity distribution involved. In this paper, an efficient algorithm is proposed to construct semi-Bayesian -optimal mixed logit designs that take into account the uncertainty about the mean vector of the distribution. These designs are compared to locally -optimal mixed logit designs, Bayesian and locally -optimal designs for the multinomial logit model and to nearly orthogonal designs (Sawtooth (CBC)) for a wide range of parameter values. It is found that the semi-Bayesian mixed logit designs outperform the competing designs not only in terms of estimation efficiency but also in terms of prediction accuracy. In particular, it is shown that assuming large prior values for the variance parameters for constructing semi-Bayesian mixed logit designs is most robust against the misspecification of the prior mean vector. In addition, the semi-Bayesian mixed logit designs are compared to the fully Bayesian mixed logit designs, which take also into account the uncertainty about the variances in the heterogeneity distribution and which can be constructed only using prohibitively large computing power. The differences in estimation and prediction accuracy turn out to be rather small in most cases, which indicates that the semi-Bayesian approach is currently the most appropriate one if one needs to estimate mixed logit models.

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Bibliographic Info

Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 28 (2009)
Issue (Month): 1 (01-02)
Pages: 122-135

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Handle: RePEc:inm:ormksc:v:28:y:2009:i:1:p:122-135

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Keywords: semi-Bayesian mixed logit design; fully Bayesian mixed logit design; heterogeneity; prediction accuracy; multinomial logit design; model-robust design; -optimality; design construction algorithm;


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Cited by:
  1. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2010. "Comparing different sampling schemes for approximating the integrals involved in the efficient design of stated choice experiments," Transportation Research Part B: Methodological, Elsevier, vol. 44(10), pages 1268-1289, December.
  2. KESSELS, Roselinde & BRADLEY, Jones & GOOS, Peter, 2012. "A comparison of partial profile designs for discrete choice experiments with an application in software development," Working Papers 2012004, University of Antwerp, Faculty of Applied Economics.
  3. Fischer, Timo & Henkel, Joachim, 2013. "Complements and substitutes in profiting from innovation—A choice experimental approach," Research Policy, Elsevier, vol. 42(2), pages 326-339.
  4. John Rose & Michiel Bliemer, 2013. "Sample size requirements for stated choice experiments," Transportation, Springer, vol. 40(5), pages 1021-1041, September.
  5. Aiste Ruseckaite & Peter Goos & Dennis Fok, 2014. "Bayesian D-Optimal Choice Designs for Mixtures," Tinbergen Institute Discussion Papers 14-057/III, Tinbergen Institute.
  6. Timo Fischer & Gaétan de Rassenfosse, 2011. "Debt Financing of High-growth Startups," DRUID Working Papers 11-04, DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies.
  7. Crabbe, Marjolein & Akinc, Deniz & Vandebroek, Martina, 2014. "Fast algorithms to generate individualized designs for the mixed logit choice model," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 1-15.
  8. Bart Vermeulen & Peter Goos & Riccardo Scarpa & Martina Vandebroek, 2011. "Bayesian Conjoint Choice Designs for Measuring Willingness to Pay," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 48(1), pages 129-149, January.
  9. Crabbe, M. & Vandebroek, M., 2012. "Improving the efficiency of individualized designs for the mixed logit choice model by including covariates," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2059-2072.
  10. Bliemer, Michiel C.J. & Rose, John M., 2010. "Construction of experimental designs for mixed logit models allowing for correlation across choice observations," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 720-734, July.


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