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A Guide to Measuring and Interpreting Attribute Importance

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  • Juan Marcos Gonzalez

    (Duke University)

Abstract

Stated-preference (SP) methods, such as discrete-choice experiments (DCE) and best–worst scaling (BWS), have increasingly been used to measure preferences for attributes of medical interventions. Preference information is commonly characterized using attribute importance. However, attribute importance measures can vary in value and interpretation depending on the method used to elicit preferences, the specific context of the questions, and the approach used to normalize attribute effects. This variation complicates the interpretation of preference results and the comparability of results across subgroups in a sample. This article highlights the potential consequences of ignoring variations in attribute importance measures, and makes the case for reporting more clearly how these measures are obtained and calculated. Transparency in the calculations can clarify what conclusions are supported by the results, and help make more accurate and meaningful comparisons across subsamples.

Suggested Citation

  • Juan Marcos Gonzalez, 2019. "A Guide to Measuring and Interpreting Attribute Importance," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 12(3), pages 287-295, June.
    • Handle: RePEc:spr:patien:v:12:y:2019:i:3:d:10.1007_s40271-019-00360-3
    DOI: 10.1007/s40271-019-00360-3
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    References listed on IDEAS

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    1. LUYTEN, Jeroen & KESSELS, Roselinde & GOOS, Peter & BEUTELS, Philippe, 2013. "Public preferences for prioritizing preventive and curative health care interventions: A discrete choice experiment," Working Papers 2013032, University of Antwerp, Faculty of Business and Economics.
    2. Lancsar, Emily & Louviere, Jordan & Flynn, Terry, 2007. "Several methods to investigate relative attribute impact in stated preference experiments," Social Science & Medicine, Elsevier, vol. 64(8), pages 1738-1753, April.
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    Cited by:

    1. David J. Mott & Laura Ternent & Luke Vale, 2023. "Do preferences differ based on respondent experience of a health issue and its treatment? A case study using a public health intervention," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(3), pages 413-423, April.
    2. Kularatne, Thamarasi & Wilson, Clevo & Lee, Boon & Hoang, Viet-Ngu, 2021. "Tourists’ before and after experience valuations: A unique choice experiment with policy implications for the nature-based tourism industry," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 529-543.
    3. Pakhi Sharma & Sanjeewa Kularatna & Bridget Abell & Steven M. McPhail & Sameera Senanayake, 2024. "Preferences for Neurodevelopmental Follow-Up Care for Children: A Discrete Choice Experiment," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 17(6), pages 645-662, November.
    4. Vo, Linh K. & Allen, Michelle J. & Cunich, Michelle & Thillainadesan, Janani & McPhail, Steven M. & Sharma, Pakhi & Wallis, Shannon & McGowan, Kelly & Carter, Hannah E., 2024. "Stakeholders’ preferences for the design and delivery of virtual care services: A systematic review of discrete choice experiments," Social Science & Medicine, Elsevier, vol. 340(C).
    5. Juan M. Gonzalez Sepulveda & F. Reed Johnson & Deborah A. Marshall, 2021. "Incomplete information and irrelevant attributes in stated‐preference values for health interventions," Health Economics, John Wiley & Sons, Ltd., vol. 30(11), pages 2637-2648, November.

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