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Best-Worst Scaling in analytical closed-form solution

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  • Lipovetsky, Stan
  • Conklin, Michael

Abstract

Best-Worst Scaling (BWS), sometimes also called Maximum Difference (MaxDiff), is a discrete choice modeling method widely used for finding utilities and choice probabilities among multiple alternatives. It can be seen as an extension of the paired comparison techniques for the simultaneous presentation of several items together to respondents. A respondent identifies the best and the worst ones and estimation of utilities is performed using a multinomial-logit (MNL) model in numerical nonlinear estimations. The main contribution of this paper consists in finding an analytical closed-form solution producing an approximation of the results for utilities and choice probabilities that are obtained using MNL models. The analytical formulae permit the inference of the characteristics of the model׳s quality, including standard errors of the utilities and choice probabilities, the residual deviance and pseudo-R2. This approach enriches the BWS methods and is useful for theoretical descriptions and practical applications.

Suggested Citation

  • Lipovetsky, Stan & Conklin, Michael, 2014. "Best-Worst Scaling in analytical closed-form solution," Journal of choice modelling, Elsevier, vol. 10(C), pages 60-68.
    • Handle: RePEc:eee:eejocm:v:10:y:2014:i:c:p:60-68
    DOI: 10.1016/j.jocm.2014.02.001
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. Alexandre Brouste & Christophe Dutang & Tom Rohmer, 2022. "A Closed-form Alternative Estimator for GLM with Categorical Explanatory Variables," Post-Print hal-03689206, HAL.
    4. White, Mark H., 2021. "bwsTools: An R package for case 1 best-worst scaling," Journal of choice modelling, Elsevier, vol. 39(C).
    5. 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.
    6. Lipovetsky, Stan & Conklin, Michael, 2014. "Finding items cannibalization and synergy by BWS data," Journal of choice modelling, Elsevier, vol. 12(C), pages 1-9.
    7. 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.
    8. 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.
    9. Jinhua Li & Fang Zhang & Shiwei Sun, 2019. "Building Consumer-Oriented CSR Differentiation Strategy," Sustainability, MDPI, vol. 11(3), pages 1-14, January.

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