谁在你的调查中作弊?一种基于数字轨迹数据的检测方法

IF 2.5 2区 社会学 Q1 POLITICAL SCIENCE Political Science Research and Methods Pub Date : 2022-11-28 DOI:10.1017/psrm.2022.42
Simon Munzert, Sebastian Ramirez-Ruiz, Pablo Barberá, A. Guess, JungHwan Yang
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引用次数: 0

摘要

在本说明中,我们提供了在政治知识在线评估中作弊的直接证据。我们将调查结果与德国和美国在线小组的网络跟踪数据相结合,以评估人们是否会求助于外部来源寻求答案。我们观察到,根据问题类型和难度的不同,项目级作弊的发生率从0%到12%不等,发现23%的受访者至少有过一次跨波作弊。在采用承诺承诺的美国小组中,我们观察到不到1%的受访者有作弊行为。我们发现了与作弊相关的强大的受访者和项目级别的特征。然而,项目级别的作弊事件是罕见的;因此,如果没有跟踪数据,它们很难预测和校正。即便如此,我们对天真和作弊纠正的政治知识衡量标准的比较分析提供了证据,证明作弊并没有实质性地扭曲推论。
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Who's cheating on your survey? A detection approach with digital trace data
In this note, we provide direct evidence of cheating in online assessments of political knowledge. We combine survey responses with web tracking data of a German and a US online panel to assess whether people turn to external sources for answers. We observe item-level prevalence rates of cheating that range from 0 to 12 percent depending on question type and difficulty, and find that 23 percent of respondents engage in cheating at least once across waves. In the US panel, which employed a commitment pledge, we observe cheating behavior among less than 1 percent of respondents. We find robust respondent- and item-level characteristics associated with cheating. However, item-level instances of cheating are rare events; as such, they are difficult to predict and correct for without tracking data. Even so, our analyses comparing naive and cheating-corrected measures of political knowledge provide evidence that cheating does not substantially distort inferences.
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来源期刊
CiteScore
8.10
自引率
0.00%
发文量
54
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