Stated versus revealed preferences: An approach to reduce bias
研究利用大规模常规数据帮助陈述偏好调查更准确预测实际行为，通过迭代调查设计减少假设偏差，以英格兰献血服务偏好调查为例验证效果。
Stated preference (SP) survey responses may not predict actual behavior, leading to hypothetical bias. We developed an approach that harnesses large-scale routine data to help SP surveys provide more accurate estimates of revealed preferences (RPs), within a study which elicited preferences for alternative changes to the blood service in England. The SP survey responses were used to predict the mean number of annual whole blood donations. Ex ante, the iterative survey design estimated hypothetical bias by contrasting pilot SP survey responses (N = 1254), with individually linked data on RPs, to inform the main SP survey design (N = 25,187). Ex post, the analysis recognized mediation of the relationship between SP and RP when blood donation is deferred. The pilot survey reported that donors' intended donation frequency of 3.2 (men) and 2.6 (women) times per year, exceeded their actual frequency by 41% and 30% respectively. Choice scenario attributes for the main SP survey were then modified, and over-prediction subsequently decreased to 34% for men and 16% for women. The mediating effect of deferrals explained 29% (men) and 86% (women) of the residual discrepancy between SP and RP. Future studies can use this approach to reduce hypothetical bias, and provide more accurate predictions for decision-making.