Assessing individual-level theories of electoral participation requires survey-based measures of turnout. Yet, due to a combination of sampling problems and respondent misreporting, postelection surveys routinely overestimate turnout, often by large margins. Using an online survey experiment fielded after the 2015 British general election, we implement three alternative survey questions aimed at correcting for turnout misreporting and test them against a standard direct turnout question used in postelection studies. Comparing estimated to actual turnout rates, we find that while all question designs overestimate aggregate turnout, the item-count technique alleviates the misreporting problem substantially, whereas a direct turnout question with additional face-saving options and a crosswise model design help little or not at all. Also, regression models of turnout estimated using the item-count measure yield substantively similar inferences regarding the correlates of electoral participation to models estimated using “gold-standard” validated vote measures. These findings stand in contrast to those suggesting that item-count techniques do not help with misreporting in an online setting and are particularly relevant given the increasing use of online surveys in election studies.

中文翻译：

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减少在线调查中的投票率误报

评估个人层面的选举参与理论需要基于调查的投票率测量。然而，由于抽样问题和受访者误报的综合作用，选举后的调查经常高估投票率，而且往往高估。使用 2015 年英国大选后进行的在线调查实验，我们实施了三个替代调查问题，旨在纠正投票率误报，并针对选举后研究中使用的标准直接投票率问题对其进行测试。将估计的投票率与实际投票率进行比较，我们发现虽然所有问题设计都高估了总投票率，但项目计数技术大大缓解了误报问题，而具有额外面子选项和横向模型设计的直接投票率问题在全部。还，使用项目计数度量估计的投票率回归模型产生了关于选举参与相关性的推论与使用“黄金标准”验证投票度量估计的模型基本相似。这些发现与那些暗示项目计数技术无助于在线环境中的误报的发现形成对比，并且鉴于在选举研究中越来越多地使用在线调查，这些发现尤其相关。