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, that is, 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 covariate 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：投票率高估一直是选举调查中的长期问题，其中无应答偏差或选民过度代表被视为主要偏差来源。然而，调整不可忽略的无应答偏差极具挑战性。基于2020年美国总统大选的ANES无应答追踪研究，我们探究了回访数据（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。我们提出了一个"抵制稳定性假设"来解释结果变量中的不可忽略缺失机制，该假设指出缺失结果对响应倾向的影响在前两次回访尝试中保持稳定。在此假设下，结合人口普查数据的协变量信息，我们建立了可识别性并开发了投票率估计方法。我们的方法得出的估计值与官方投票率非常接近，并成功捕捉到随响应抗拒程度增加，投票意愿下降的趋势。这项工作凸显了调整不可忽略无应答偏差的重要性，并展示了广泛可获取的回访数据在政治调查中的应用潜力。