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Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues

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

Abstract

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. Copyright Mühlbacher et al. 2015

Suggested Citation

  • Axel Mühlbacher & Peter Zweifel & Anika Kaczynski & F. Johnson, 2015. "Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues," Health Economics Review, Springer, vol. 6(1), pages 1-12, December.
    • Handle: RePEc:spr:hecrev:v:6:y:2015:i:1:p:1-12:10.1186/s13561-015-0077-z
    DOI: 10.1186/s13561-015-0077-z
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    References listed on IDEAS

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    1. Pat Auger & Timothy Devinney & Jordan Louviere, 2007. "Using Best–Worst Scaling Methodology to Investigate Consumer Ethical Beliefs Across Countries," Journal of Business Ethics, Springer, vol. 70(3), pages 299-326, February.
    2. Marti, Joachim, 2012. "A best–worst scaling survey of adolescents' level of concern for health and non-health consequences of smoking," Social Science & Medicine, Elsevier, vol. 75(1), pages 87-97.
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    4. Deborah Marshall & John Bridges & Brett Hauber & Ruthanne Cameron & Lauren Donnalley & Ken Fyie & F. Reed Johnson, 2010. "Conjoint Analysis Applications in Health — How are Studies being Designed and Reported?," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 3(4), pages 249-256, December.
    5. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D. With contributions by-Name:Adamowicz,Wiktor, 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304.
    6. F. Reed Johnson & Ateesha F. Mohamed & Semra Özdemir & Deborah A. Marshall & Kathryn A. Phillips, 2011. "How does cost matter in health‐care discrete‐choice experiments?," Health Economics, John Wiley & Sons, Ltd., vol. 20(3), pages 323-330, March.
    7. Harry Telser & Peter Zweifel, 2007. "Validity of discrete-choice experiments evidence for health risk reduction," Applied Economics, Taylor & Francis Journals, vol. 39(1), pages 69-78.
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