Best-worst scaling: What it can do for health care research and how to do it

Flynn, TN; Louviere, JJ; Peters, TJ; Coast, J

Best-worst scaling: What it can do for health care research and how to do it

Flynn, TN Louviere, JJ Peters, TJ Coast, J

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Publication Type:: Journal Article
Citation:: Journal of Health Economics, 2007, 26 (1), pp. 171 - 189
Issue Date:: 2007-01-01

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Field	Value	Language
dc.contributor.author	Flynn, TN	en_US
dc.contributor.author	Louviere, JJ	en_US
dc.contributor.author	Peters, TJ	en_US
dc.contributor.author	Coast, J	en_US
dc.date.available	2006-04-19	en_US
dc.date.issued	2007-01-01	en_US
dc.identifier.citation	Journal of Health Economics, 2007, 26 (1), pp. 171 - 189	en_US
dc.identifier.issn	0167-6296	en_US
dc.identifier.uri	http://hdl.handle.net/10453/5115
dc.description.abstract	Statements like "quality of care is more highly valued than waiting time" can neither be supported nor refuted by comparisons of utility parameters from a traditional discrete choice experiment (DCE). Best-worst scaling can overcome this problem because it asks respondents to perform a different choice task. However, whilst the nature of the best-worst task is generally understood, there are a number of issues relating to the design and analysis of a best-worst choice experiment that require further exposition. This paper illustrates how to aggregate and analyse such data and using a quality of life pilot study demonstrates how richer insights can be drawn by the use of best-worst tasks. © 2006 Elsevier B.V. All rights reserved.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0343632
dc.relation.ispartof	Journal of Health Economics	en_US
dc.relation.isbasedon	10.1016/j.jhealeco.2006.04.002	en_US
dc.subject.classification	Health Policy & Services	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Quality of Life	en_US
dc.subject.mesh	Health Services Research	en_US
dc.subject.mesh	Consumer Behavior	en_US
dc.subject.mesh	United Kingdom	en_US
dc.title	Best-worst scaling: What it can do for health care research and how to do it	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	26	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	1117 Public Health and Health Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Strength - CENSOC - Study of Choice
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

Statements like "quality of care is more highly valued than waiting time" can neither be supported nor refuted by comparisons of utility parameters from a traditional discrete choice experiment (DCE). Best-worst scaling can overcome this problem because it asks respondents to perform a different choice task. However, whilst the nature of the best-worst task is generally understood, there are a number of issues relating to the design and analysis of a best-worst choice experiment that require further exposition. This paper illustrates how to aggregate and analyse such data and using a quality of life pilot study demonstrates how richer insights can be drawn by the use of best-worst tasks. © 2006 Elsevier B.V. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/5115