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Mühlbacher AC, Kaczynski A, Zweifel P, et al. Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview. Health Econ Rev. 2016;6(1):2doi: 10.1186/s13561-015-0079-x.
Mühlbacher, A. C., Kaczynski, A., Zweifel, P., & Johnson, F. R. (2016). Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview. Health economics review, 6(1), 2. https://doi.org/10.1186/s13561-015-0079-x
Mühlbacher, Axel C, et al. "Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview." Health economics review vol. 6,1 (2016): 2. doi: https://doi.org/10.1186/s13561-015-0079-x
Mühlbacher AC, Kaczynski A, Zweifel P, Johnson FR. Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview. Health Econ Rev. 2016 Dec;6(1):2. doi: 10.1186/s13561-015-0079-x. Epub 2016 Jan 08. PMID: 26743636; PMCID: PMC4705077.
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Health Econ Rev. 2016 Dec;6(1):2. doi: 10.1186/s13561-015-0079-x. Epub 2016 Jan 08.

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

Health economics review

Axel C Mühlbacher, Anika Kaczynski, Peter Zweifel, F Reed Johnson

Affiliations

  1. IGM Institute for Health Economics and Health Care Management, Hochschule Neubrandenburg, Neubrandenburg, Germany. [email protected].
  2. IGM Institute for Health Economics and Health Care Management, Hochschule Neubrandenburg, Neubrandenburg, Germany. [email protected].
  3. Department of Economics, University of Zürich, Zürich, Switzerland. [email protected].
  4. Center for Clinical and Genetic Economics, Duke Clinical Research Institute, Duke University, Durham, USA. [email protected].

PMID: 26743636 PMCID: PMC4705077 DOI: 10.1186/s13561-015-0079-x

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.

Keywords: BWS; Best-worst scaling; Experimental measurement; Healthcare decision making; Patient preferences

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