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Taking the Shortcut: Simplifying Heuristics in Discrete Choice Experiments.
Jorien Veldwijk1,2,3, Stella Maria Marceta4,5,6, Joffre Dan Swait4,5,6
1Erasmus School of Health Policy & Management, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands. veldwijk@eshpm.eur.nl.
The Patient
|May 2, 2023
Summary
Health-related discrete choice experiments (DCEs) can be improved by understanding how people use simplifying heuristics. This study overviews heuristics and suggests design and modeling strategies to account for them.
Area of Science:
- Health Economics
- Behavioral Economics
- Decision Science
Background:
- Discrete Choice Experiments (DCEs) are vital for health policy and product development.
- Standard DCE analysis assumes rational choice behavior based on Random Utility Theory (RUT).
- Respondents may use simplifying heuristics, deviating from RUT's assumptions.
Purpose of the Study:
- To overview common simplifying heuristics in health-related DCEs.
- To examine how DCE design, context, and population affect heuristic use.
- To propose DCE design and modeling strategies to identify and address heuristic impacts.
Main Methods:
- Literature review of simplifying heuristics in health DCEs.
- Analysis of factors influencing heuristic use in choice tasks.
- Development of design and modeling recommendations for DCEs.
Main Results:
- Identified common simplifying heuristics used by respondents in health DCEs.
- Highlighted the influence of choice task design, context, and participant characteristics on heuristic application.
- Proposed practical strategies for DCE design and data analysis to accommodate heuristic behavior.
Conclusions:
- Acknowledging and addressing simplifying heuristics can enhance the validity and utility of health-related DCEs.
- Improved DCE design and modeling can lead to more accurate insights for health decision-making.
- This research provides a framework for researchers to better understand and manage respondent behavior in DCE studies.
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