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A Guide to Observable Differences in Stated Preference Evidence

Author

Listed:
  • Benjamin Matthew Craig

    (University of South Florida)

  • Esther W. Bekker-Grob

    (Erasmus University Rotterdam)

  • Juan Marcos González Sepúlveda

    (Duke University)

  • William H. Greene

    (New York University)

Abstract

Background and Objective In health preference research, studies commonly hypothesize differences in parameters (i.e., differential or joint effects on attribute importance) and/or in choice predictions (marginal effects) by observable factors. Discrete choice experiments may be designed and conducted to test and estimate these observable differences. This guide covers how to explore and corroborate various observable differences in health preference evidence. Methods The analytical process has three steps: analyze the exploratory data, analyze the confirmatory data, and interpret and disseminate the evidence. In this guide, we demonstrate the process using dual samples (where exploratory and confirmatory samples were collected from different sources) on 2020 US COVID-19 vaccination preferences; however, investigators may apply the same approach using split samples (i.e., single source). Results The confirmatory analysis failed to reject ten of the 17 null hypotheses generated by the exploratory analysis (p

Suggested Citation

  • Benjamin Matthew Craig & Esther W. Bekker-Grob & Juan Marcos González Sepúlveda & William H. Greene, 2022. "A Guide to Observable Differences in Stated Preference Evidence," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 15(3), pages 329-339, May.
    • Handle: RePEc:spr:patien:v:15:y:2022:i:3:d:10.1007_s40271-021-00551-x
    DOI: 10.1007/s40271-021-00551-x
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