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

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• 作者：Axel C. Mühlbacher ; Anika Kaczynski ; Peter Zweifel…
• 关键词：Best ; worst scaling ; BWS ; Experimental measurement ; Healthcare decision making ; Patient preferences
• 刊名：Health Economics Review
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：6
• 期：1
• 全文大小：1,075 KB
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• 作者单位：Axel C. Mühlbacher (1)
  Anika Kaczynski (1)
  Peter Zweifel (2)
  F. Reed Johnson (3)
  
  1. IGM Institute for Health Economics and Health Care Management, Hochschule Neubrandenburg, Neubrandenburg, Germany
  2. Department of Economics, University of Zürich, Zürich, Switzerland
  3. Center for Clinical and Genetic Economics, Duke Clinical Research Institute, Duke University, Durham, USA
  
• 刊物主题：Public Health; Economic Policy; Public Finance & Economics; Health Informatics; Statistics for Life Sciences, Medicine, Health Sciences; Statistics for Business/Economics/Mathematical Finance/Insurance;
• 出版者：Springer Berlin Heidelberg
• ISSN：2191-1991

文摘

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