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Stated versus revealed preferences: An approach to reduce bias
Kaat de Corte1, John Cairns1, Richard Grieve1
1Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK.
Health Economics
|March 10, 2021
Summary
Stated preference (SP) surveys often overestimate real behavior, causing hypothetical bias. This study used routine data to improve SP surveys, reducing bias in predicting blood donation behavior and enhancing decision-making accuracy.
Area of Science:
- Behavioral Economics
- Health Services Research
- Survey Methodology
Background:
- Stated preference (SP) surveys can suffer from hypothetical bias, where stated intentions do not accurately reflect revealed preferences (RPs) or actual behavior.
- This bias can lead to inaccurate predictions in decision-making, particularly in public health contexts like blood donation.
- Large-scale routine data offers a potential resource to calibrate SP surveys and mitigate hypothetical bias.
Purpose of the Study:
- To develop and evaluate an approach using routine data to reduce hypothetical bias in SP surveys.
- To improve the accuracy of SP survey estimates for predicting actual behavior, specifically blood donation frequency.
- To inform the design of SP surveys for more reliable preference elicitation.
Main Methods:
- An iterative survey design was employed, using pilot SP data linked with routine revealed preference (RP) data to estimate and correct for hypothetical bias.
- Pilot survey responses (N=1254) were compared to actual donation data to quantify initial bias.
- The main SP survey (N=25,187) incorporated design modifications based on pilot findings, and post-hoc analysis examined the mediating role of donation deferrals.
Main Results:
- Initial pilot surveys showed intended donation frequencies significantly overestimated actual behavior (41% for men, 30% for women).
- Modifications to the main SP survey design reduced this overestimation (34% for men, 16% for women).
- Donation deferrals were found to mediate the relationship between SP and RP, explaining a substantial portion of the remaining discrepancy (29% for men, 86% for women).
Conclusions:
- Harnessing large-scale routine data can effectively reduce hypothetical bias in stated preference surveys.
- An iterative, data-informed survey design process improves the accuracy of predicting revealed preferences and actual behavior.
- This methodology provides a robust framework for enhancing the predictive validity of SP surveys in future decision-making studies.
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