Replication Data for: The Misreporting Trade-off Between List Experiments and Direct Questions in Practice: Partition Validation Evidence from Two Countries

To reduce strategic misreporting on sensitive topics, survey researchers increasingly use list experiments rather than direct questions. However, the complexity of list experiments may increase non-strategic misreporting. We provide the first empirical assessment of this trade-off between strategic and non-strategic misreporting. We field list experiments on election turnout in two different countries, collecting measures of respondents' true turnout. We detail and apply a partition validation method which uses true scores to distinguish true and false positives and negatives for list experiments, thus allowing detection of non-strategic reporting errors. For both list experiments, partition validation reveals non-strategic misreporting that is: undetected by standard diagnostics or validation; greater than assumed in extant simulation studies; and severe enough that direct turnout questions subject to strategic misreporting exhibit lower overall reporting error. We discuss how our results can inform the choice between list experiment and direct question for other topics and survey contexts.为减少敏感话题上的策略性谎报（strategic misreporting），调查研究者愈发倾向于使用列表实验（list experiment）而非直接提问法。然而，列表实验的复杂性可能会加剧非策略性误报（non-strategic misreporting）。我们首次针对策略性谎报与非策略性误报间的这一权衡开展实证评估。我们在两个不同国家实地开展了针对选举投票率的列表实验，并收集了受访者真实投票率的测度数据。我们详细阐释并应用了分区验证法（partition validation method），该方法可借助真实得分区分列表实验中的真阳性与真阴性（true positives and negatives）结果，从而实现非策略性报告误差的检测。针对两项列表实验，分区验证法均揭示出如下非策略性误报特征：其一，标准诊断方法（standard diagnostics）与常规验证手段无法识别此类误报；其二，其发生规模高于现有模拟研究（extant simulation studies）中的假设水平；其三，其严重程度足以令易受策略性谎报干扰的直接投票率问题，整体报告误差反而更低。我们还讨论了本研究结果可如何为其他话题与调查场景下，列表实验与直接提问法的选择提供决策参考。