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Preferences for public involvement in health service decisions: a comparison between best-worst scaling and trio-wise stated preference elicitation techniques

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  • Seda Erdem

    (University of Stirling)

  • Danny Campbell

    (University of Stirling)

Abstract

Stated preference elicitation techniques, such as discrete choice experiments and best-worst scaling, are now widely used in health research to explore the public’s choices and preferences. In this paper, we propose an alternative stated preference elicitation technique, which we refer to as ‘trio-wise’. We explain this new technique, its relative advantages, modeling framework, and how it compares to the best-worst scaling method. To better illustrate the differences and similarities, we utilize best-worst scaling Case 2, where individuals make best and worst (most and least) choices for the attribute levels that describe a single profile. We demonstrate this new preference elicitation technique using an empirical case study that explores preferences among the general public for ways to involve them in decisions concerning the health care system. Our findings show that the best-worst scaling and trio-wise preference elicitation techniques both retrieve similar preferences. However, the capability of our trio-wise method to provide additional information on the strength of rank preferences and its ability to accommodate indifferent preferences lead us to prefer it over the standard best-worst scaling technique.

Suggested Citation

  • Seda Erdem & Danny Campbell, 2017. "Preferences for public involvement in health service decisions: a comparison between best-worst scaling and trio-wise stated preference elicitation techniques," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(9), pages 1107-1123, December.
  • Handle: RePEc:spr:eujhec:v:18:y:2017:i:9:d:10.1007_s10198-016-0856-4
    DOI: 10.1007/s10198-016-0856-4
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    References listed on IDEAS

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    8. Campbell, Danny & Boeri, Marco & Doherty, Edel & George Hutchinson, W., 2015. "Learning, fatigue and preference formation in discrete choice experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 345-363.
    9. Marti, Joachim, 2012. "A best–worst scaling survey of adolescents' level of concern for health and non-health consequences of smoking," Social Science & Medicine, Elsevier, vol. 75(1), pages 87-97.
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    12. Danny Campbell & Seda Erdem, 2015. "Position Bias in Best-worst Scaling Surveys: A Case Study on Trust in Institutions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(2), pages 526-545.
    13. Hess, Stephane & Bierlaire, Michel & Polak, John W., 2005. "Estimation of value of travel-time savings using mixed logit models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(2-3), pages 221-236.
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    Cited by:

    1. Rebecca Kandiyali & Annie Hawton & Christie Cabral & Julie Mytton & Valerie Shilling & Christopher Morris & Jenny Ingram, 2019. "Working with Patients and Members of the Public: Informing Health Economics in Child Health Research," PharmacoEconomics - Open, Springer, vol. 3(2), pages 133-141, June.
    2. Ivan Sever & Miroslav Verbič & Eva Klaric Sever, 2020. "Estimating Attribute-Specific Willingness-to-Pay Values from a Health Care Contingent Valuation Study: A Best–Worst Choice Approach," Applied Health Economics and Health Policy, Springer, vol. 18(1), pages 97-107, February.

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