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Optimal approximate choice designs for a two‐step coffee choice, taste and choice again experiment

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  • Nedka Dechkova Nikiforova
  • Rossella Berni
  • Jesús Fernando López‐Fidalgo

Abstract

This work deals with consumers' preferences about coffee. Firstly, a choice experiment is performed on a sample of potential consumers. Following this, a sensory test involving the tasting of two varieties of coffee is carried out with the respondents, after which the same choice experiment is supplied to them again. An innovative approach for building heterogeneous choice designs is specifically developed for the case‐study, based on approximate design theory and compound design criterion. Panel Mixed Logit models are used, thereby allowing for the inclusion of correlation among consumers' responses; choice‐sets are supplied to a proportion of respondents according to optimal weights. The estimation results of the Panel Mixed Logit model are satisfactory, confirming the validity of the proposed approach.

Suggested Citation

  • Nedka Dechkova Nikiforova & Rossella Berni & Jesús Fernando López‐Fidalgo, 2022. "Optimal approximate choice designs for a two‐step coffee choice, taste and choice again experiment," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1895-1917, November.
  • Handle: RePEc:bla:jorssc:v:71:y:2022:i:5:p:1895-1917
    DOI: 10.1111/rssc.12601
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    References listed on IDEAS

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    1. 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.
    2. Arora, Neeraj & Huber, Joel, 2001. "Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(2), pages 273-283, September.
    3. Jie Yu & Peter Goos & Martina Vandebroek, 2009. "Efficient Conjoint Choice Designs in the Presence of Respondent Heterogeneity," Marketing Science, INFORMS, vol. 28(1), pages 122-135, 01-02.
    4. Zsolt Sándor & Michel Wedel, 2002. "Profile Construction in Experimental Choice Designs for Mixed Logit Models," Marketing Science, INFORMS, vol. 21(4), pages 455-475, February.
    5. Kessels, Roselinde & Jones, Bradley & Goos, Peter & Vandebroek, Martina, 2009. "An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 279-291.
    6. Qing Liu & Yihui (Elina) Tang, 2015. "Construction of Heterogeneous Conjoint Choice Designs: A New Approach," Marketing Science, INFORMS, vol. 34(3), pages 346-366, May.
    7. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    8. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, October.
    9. García-Ródenas, Ricardo & García-García, José Carlos & López-Fidalgo, Jesús & Martín-Baos, José Ángel & Wong, Weng Kee, 2020. "A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    10. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    11. Min Yang & Stefanie Biedermann & Elina Tang, 2013. "On Optimal Designs for Nonlinear Models: A General and Efficient Algorithm," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1411-1420, December.
    12. James J. Heckman, 1981. "Heterogeneity and State Dependence," NBER Chapters, in: Studies in Labor Markets, pages 91-140, National Bureau of Economic Research, Inc.
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