Randomized response techniques (RRTs) are applied to reduce response biases in self-report surveys on sensitive research questions (e.g., on socially undesirable characteristics). However, there is evidence that they cannot completely eliminate self-protecting response strategies. To address this problem, there are RRTs specifically designed to measure the extent of such strategies. Here we assessed the recently devised unrelated question model—cheating extension (UQMC) in a preregistered online survey on intimate partner violence (IPV) victimization and perpetration during the first contact restrictions as containment measures for the outbreak of the coronavirus disease 2019 pandemic in Germany in early 2020. The UQMC accounting for self-protecting responses described the data better than its predecessor model which assumes instruction adherence. The resulting three-month prevalence estimates were about 10% and we found a high proportion of self-protecting responses in the group of female participants queried about IPV victimization. However, unexpected results concerning the differences in prevalence estimates across the groups queried about victimization and perpetration highlight the difficulty of investigating sensitive research questions even using methods that guarantee anonymity and the importance of interpreting the respective estimates with caution.

随机响应技术 (RRT) 用于减少对敏感研究问题（例如，社会不良特征）的自我报告调查中的响应偏差。然而，有证据表明它们不能完全消除自我保护反应策略。为了解决这个问题，有专门设计的 RRT 来衡量这种策略的程度。在这里，我们在一项预先注册的在线调查中评估了最近设计的不相关问题模型 - 作弊扩展 (UQMC)，该调查将首次接触限制期间的亲密伴侣暴力 (IPV) 受害和实施作为遏制 2019 年德国冠状病毒病大流行的措施。 2020 年初。考虑到自我保护响应的 UQMC 对数据的描述比其假设遵守指令的前任模型更好。由此产生的三个月流行率估计约为 10%，我们发现在询问 IPV 受害问题的女性参与者组中，自我保护反应的比例很高。然而，关于受害和犯罪调查组的流行率估计差异的意外结果凸显了即使使用保证匿名的方法调查敏感研究问题的困难，以及谨慎解释各自估计的重要性。