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Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview

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  • Axel Mühlbacher
  • Anika Kaczynski
  • Peter Zweifel
  • F. Johnson

Abstract

Best-worst scaling (BWS), also known as maximum-difference scaling, is a multiattribute approach to measuring preferences. BWS aims at the analysis of preferences regarding a set of attributes, their levels or alternatives. It is a stated-preference method based on the assumption that respondents are capable of making judgments regarding the best and the worst (or the most and least important, respectively) out of three or more elements of a choice-set. As is true of discrete choice experiments (DCE) generally, BWS avoids the known weaknesses of rating and ranking scales while holding the promise of generating additional information by making respondents choose twice, namely the best as well as the worst criteria. A systematic literature review found 53 BWS applications in health and healthcare. This article expounds possibilities of application, the underlying theoretical concepts and the implementation of BWS in its three variants: ‘object case’, ‘profile case’, ‘multiprofile case’. This paper contains a survey of BWS methods and revolves around study design, experimental design, and data analysis. Moreover the article discusses the strengths and weaknesses of the three types of BWS distinguished and offered an outlook. A companion paper focuses on special issues of theory and statistical inference confronting BWS in preference measurement. Copyright Mühlbacher et al. 2015

Suggested Citation

  • Axel Mühlbacher & Anika Kaczynski & Peter Zweifel & F. Johnson, 2015. "Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview," Health Economics Review, Springer, vol. 6(1), pages 1-14, December.
    • Handle: RePEc:spr:hecrev:v:6:y:2015:i:1:p:1-14:10.1186/s13561-015-0079-x
    DOI: 10.1186/s13561-015-0079-x
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    References listed on IDEAS

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