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A formal and empirical comparison of two score measures for best–worst scaling

Author

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  • Marley, A.A.J.
  • Islam, T.
  • Hawkins, G.E.

Abstract

Best–worst scaling (BWS) is a method that asks individuals to choose the most and the least preferred option from a set of available options. There has been extensive discussion and evaluation of the use of scores (data summaries) in the analysis of such data. Here we motivate, summarize, and compare the usefulness of two such score measures: the analytical closed form solution (Lipovetsky and Conklin, 2014, Journal of Choice Modelling) and normalized best–worst scores (Louviere et al., 2015, Cambridge University Press). We conclude that both have underlying motivations in the maxdiff model of best–worst choice and that the analytical closed form solution provides better fits to the aggregate choices in several best–worst choice data sets.

Suggested Citation

  • Marley, A.A.J. & Islam, T. & Hawkins, G.E., 2016. "A formal and empirical comparison of two score measures for best–worst scaling," Journal of choice modelling, Elsevier, vol. 21(C), pages 15-24.
    • Handle: RePEc:eee:eejocm:v:21:y:2016:i:c:p:15-24
    DOI: 10.1016/j.jocm.2016.03.002
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    References listed on IDEAS

    as
    1. Lipovetsky, Stan & Conklin, Michael, 2014. "Best-Worst Scaling in analytical closed-form solution," Journal of choice modelling, Elsevier, vol. 10(C), pages 60-68.
    2. Islam, Towhidul, 2014. "Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data," Energy Policy, Elsevier, vol. 65(C), pages 340-350.
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    Cited by:

    1. White, Mark H., 2021. "bwsTools: An R package for case 1 best-worst scaling," Journal of choice modelling, Elsevier, vol. 39(C).
    2. Lipovetsky, Stan, 2018. "Quantum paradigm of probability amplitude and complex utility in entangled discrete choice modeling," Journal of choice modelling, Elsevier, vol. 27(C), pages 62-73.
    3. Echaniz, Eneko & Ho, Chinh Q. & Rodriguez, Andres & dell'Olio, Luigi, 2019. "Comparing best-worst and ordered logit approaches for user satisfaction in transit services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 752-769.
    4. Chrzan, Keith & Peitz, Megan, 2019. "Best-Worst Scaling with many items," Journal of choice modelling, Elsevier, vol. 30(C), pages 61-72.
    5. Amanda Working & Mohammed Alqawba & Norou Diawara, 2020. "Dynamic Attribute-Level Best Worst Discrete Choice Experiments," International Journal of Marketing Studies, Canadian Center of Science and Education, vol. 11(2), pages 1-1, March.
    6. Alexandre Brouste & Christophe Dutang & Tom Rohmer, 2022. "A Closed-form Alternative Estimator for GLM with Categorical Explanatory Variables," Post-Print hal-03689206, HAL.

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