Overestimation of turnout has long been an issue in election surveys, with nonresponse bias or voter overrepresentation identified as major sources of bias. However, adjusting for nonignorable nonresponse bias is substantially challenging. Based on the ANES Non-Response Follow-Up study concerning the 2020 U.S. presidential election, we investigate the role of callback data, i.e., records of contact attempts in the survey course, in adjusting for nonresponse bias in the estimation of turnout. We propose a stableness of resistance assumption to account for nonignorable missingness in the outcome, which states that the impact of the missing outcome on the response propensity is stable in the first two call attempts. Under this assumption and by integrating with covariates information from the census data, we establish identifiability and develop estimation methods for turnout. Our methods produce estimates very close to the official turnout and successfully capture the trend of declining willingness to vote as response reluctance increases. This work highlights the importance of adjusting for nonignorable nonresponse bias and demonstrates the potential of widely available callback data for political surveys.

翻译：选举调查中长期存在投票率高估问题，其中无应答偏差或选民过度代表被确认为主要偏差来源。然而，针对不可忽略的无应答偏差进行调整具有显著挑战性。基于2020年美国总统选举的ANES无应答追踪研究，我们探讨了回访数据（即调查过程中联系尝试的记录）在调整投票率估计中无应答偏差的作用。我们提出‘抵抗稳定性假设’以解释结果变量的不可忽略缺失，该假设认为缺失结果对应答倾向的影响在首次两次联系尝试中保持稳定。在此假设下，并结合人口普查数据中的协变量信息，我们建立了投票率的可识别性条件并开发了估计方法。我们的方法得出与官方投票率高度接近的估计值，并成功捕捉到随着应答意愿降低而呈现的投票意愿递减趋势。本研究强调了调整不可忽略无应答偏差的重要性，并展示了广泛可用的回访数据在政治调查中的应用潜力。