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An Overview of Data Collection in Health Preference Research

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Abstract

This paper focuses on survey administration and data collection methods employed for stated-preference studies in health applications. First, it describes different types of survey administration methods, encompassing web-based surveys, face-to-face (in-person) surveys, and mail surveys. Second, the concept of sampling frames is introduced, clarifying distinctions between the target population and survey frame population. The discussion then extends to different types of sampling methods, such as probability and non-probability sampling, along with an evaluation of potential issues associated with different sampling methods within the context of health preference research. Third, the paper provides information about different recruitment methods, including web-surveys, leveraging patient groups, and in-clinic recruitment. Fourth, a crucial aspect addressed is the calculation of response rate, with insights into determining an adequate response rate and strategies to improve response rates in stated-preference surveys. Lastly, the paper concludes by discussing data management plans and suggesting insights for future research in this field. In summary, this paper examines the nuanced aspects of survey administration and data collection methods in stated-preference studies, offering valuable guidance for researchers and practitioners in the health domain.

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Acknowledgements

We would like to thank Marieke Weernink for her comment in an earlier version of the paper.

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Authors and Affiliations

  1. Department of Population Health Sciences, Duke Clinical Research Institute, Duke University, Durham, NC, USA

    Semra Ozdemir

  2. Evidera Inc, London, England, UK

    Matthew Quaife

  3. Bayer Healthcare, Whippany, NJ, USA

    Ateesha F. Mohamed

  4. Curtin School of Population Health, Curtin University, Perth, Australia

    Richard Norman

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Ozdemir, S., Quaife, M., Mohamed, A.F. et al. An Overview of Data Collection in Health Preference Research. Patient (2024). https://doi.org/10.1007/s40271-024-00695-6

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