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Too many attributes: A test of the validity of combining discrete-choice and best–worst scaling data

Author

Listed:
  • Zhang, Jing
  • Reed Johnson, F.
  • Mohamed, Ateesha F.
  • Hauber, A. Brett

Abstract

Best-practice guidelines for stated-preference methods suggest there is a limit to the number of attributes respondents can reliably evaluate. This study explores a cost-effective solution to combining elicitation formats from a single study to obtain more preference information from a given sample while limiting respondents’ cognitive burden.

Suggested Citation

  • Zhang, Jing & Reed Johnson, F. & Mohamed, Ateesha F. & Hauber, A. Brett, 2015. "Too many attributes: A test of the validity of combining discrete-choice and best–worst scaling data," Journal of choice modelling, Elsevier, vol. 15(C), pages 1-13.
    • Handle: RePEc:eee:eejocm:v:15:y:2015:i:c:p:1-13
    DOI: 10.1016/j.jocm.2014.12.001
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    2. Cheng, Haotian & Zhang, Tong & Lambert, Dayton M. & Feuz, Ryan, 2023. "An empirical comparison of conjoint and best-worst scaling case III methods," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 106(C).
    3. Mo Chen & Rudy X. J. Liu & Chaochao Liu, 2021. "How to Improve the Market Penetration of New Energy Vehicles in China: An Agent-Based Model with a Three-Level Variables Structure," Sustainability, MDPI, vol. 13(21), pages 1-17, November.
    4. Matthews, Yvonne, 2023. "A hybrid and hierarchical stated preference study of freshwater restoration in Aotearoa New Zealand," Ecological Economics, Elsevier, vol. 203(C).
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    7. Arun Ulahannan & Stewart Birrell, 2022. "Designing Better Public Transport: Understanding Mode Choice Preferences Following the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(10), pages 1-15, May.

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