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Curbing curbstoning: Distributional methods to detect survey data fabrication by third-parties.
Psychological Methods ( IF 10.929 ) Pub Date : 2021-08-26 , DOI: 10.1037/met0000403
Ivan Hernandez 1 , Teresa Ristow 1 , Matthew Hauenstein 2
Affiliation  

Curbstoning, the willful fabrication of survey responses by outside data collectors, threatens the integrity of the inferences drawn from data. Researchers who outsource data collection to survey collection panels, field interviewers, or research assistants should validate whether each collection agent actually collected the data. Our review of the survey auditing literature demonstrates a consistent presence of curbstoning, even at professional levels. This study proposes several general simple survey questions that have statistical distributions known a priori, as a method to detect curbstoning. By exploiting common deficiencies in statistical understanding, survey collectors imputing data to these questions can leverage empirically known distributions to determine deviation from the expected distribution of responses. We examined both authentic and fabricated surveys that included these questions and we compared the observed distributions with the expected distributions. The majority of the proposed methods had Type I error rates near or below the specified alpha level (.05). The methods demonstrated the ability to detect false responses correctly 48%–90% of the time across two samples when surveying at least 50 participants. While the methods varied in effectiveness, combining these methods demonstrated the highest statistical power, with Type I error rates lower than 1%. Additionally, even in situations with smaller sample sizes (e.g., N = 30), combining these methods allows them to be effective in detecting curbstoning. These methods provide a simple and generalizable way for researchers not present during data collection to possess accurate data. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译:

遏制路边石化:检测第三方调查数据制造的分布式方法。

限制石化,即外部数据收集者故意编造调查响应,威胁到从数据中得出的推论的完整性。将数据收集外包给调查收集小组、实地访问员或研究助理的研究人员应验证每个收集代理是否确实收集了数据。我们对调查审计文献的回顾表明,即使在专业水平上,也始终存在遏制石化现象。本研究提出了几个具有先验已知统计分布的一般简单调查问题,作为检测路边石的方法。通过利用统计理解中的常见缺陷,将数据输入这些问题的调查收集者可以利用经验上已知的分布来确定与预期响应分布的偏差。我们检查了包含这些问题的真实调查和捏造调查,并将观察到的分布与预期分布进行了比较。大多数提议的方法的 I 类错误率接近或低于指定的 alpha 水平 (.05)。在调查至少 50 名参与者时,这些方法证明了在两个样本中正确检测错误响应的能力为 48%–90%。虽然这些方法的有效性各不相同,但结合这些方法显示出最高的统计功效,I 类错误率低于 1%。此外,即使在样本量较小的情况下(例如,大多数提议的方法的 I 类错误率接近或低于指定的 alpha 水平 (.05)。在调查至少 50 名参与者时,这些方法证明了在两个样本中正确检测错误响应的能力为 48%–90%。虽然这些方法的有效性各不相同,但结合这些方法显示出最高的统计功效,I 类错误率低于 1%。此外,即使在样本量较小的情况下(例如,大多数提议的方法的 I 类错误率接近或低于指定的 alpha 水平 (.05)。在调查至少 50 名参与者时,这些方法证明了在两个样本中正确检测错误响应的能力为 48%–90%。虽然这些方法的有效性各不相同,但结合这些方法显示出最高的统计功效,I 类错误率低于 1%。此外,即使在样本量较小的情况下(例如,N = 30),结合这些方法可以有效地检测路边石。这些方法为在数据收集期间不在场的研究人员拥有准确数据提供了一种简单且可推广的方式。(PsycInfo 数据库记录 (c) 2021 APA,保留所有权利)
更新日期:2021-08-26
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