Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues.
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For optimal solutions in health care, decision makers inevitably must evaluate trade-offs, which call for multi-attribute valuation methods. Researchers have proposed using best-worst scaling (BWS) methods which seek to extract information from respondents by asking them to identify the best and worst items in each choice set. While a companion paper describes the different types of BWS, application and their advantages and downsides, this contribution expounds their relationships with microeconomic theory, which also have implications for statistical inference. This article devotes to the microeconomic foundations of preference measurement, also addressing issues such as scale invariance and scale heterogeneity. Furthermore the paper discusses the basics of preference measurement using rating, ranking and stated choice data in the light of the findings of the preceding section. Moreover the paper gives an introduction to the use of stated choice data and juxtaposes BWS with the microeconomic foundations.
Discrete Choice Experiments
Published Version (Please cite this version)10.1186/s13561-015-0077-z
Publication InfoMühlbacher, Axel C; Zweifel, Peter; Kaczynski, Anika; & Johnson, F Reed (2016). Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues. Health Econ Rev, 6(1). pp. 5. 10.1186/s13561-015-0077-z. Retrieved from https://hdl.handle.net/10161/11718.
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Professor in Population Health Sciences
F. Reed Johnson, PhD, has more than 40 years of academic and research experience in health and environmental economics. He has served on the faculties of several universities in the United States, Canada, and Sweden, and as Distinguished Fellow at Research Triangle Institute. He currently is Senior Research Scholar in the Duke Clinical Research Institute. As a staff member in the US Environmental Protection Agency’s environmental economics research program during the 1980s, Reed helped