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 < 0.05). Apart from demographic, socioeconomic, and geographic differences, political independents and persons who have never been vaccinated against influenza are among those least likely to be vaccinated (0.838 and 0.872, respectively).
CONCLUSIONS: For all researchers in health preference research, it is essential to know how to identify and corroborate observable differences. Once mastered, this skill may lead to more complex analyses of latent differences (e.g., latent classes, random parameters). This guide concludes with six questions that researchers may ask themselves when conducting such analyses or reviewing published findings of observable differences.
Bibliographical noteFunding Information:
Benjamin M. Craig provided all financial support for the project.
© 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.