Abstract Although the use of crowdsourced online panels for behavioral data collection is commonplace in media and advertising research, only recently have software advancements made it possible for researchers to easily collect implicit measures online. Motivated by the recent decline in MTurk data quality and a dearth of literature examining the use of Implicit Association Tests with crowdsourced samples, we investigate cross-sectional data from eight IAT studies conducted using various samples (Mturk, online undergraduate students, and undergraduate behavioral labs). We document relative rates of participant inattention, non-naivety, and lack of motivation between crowdsourced and traditional samples and demonstrate the ramifications of these threats to the reliability and validity of IAT results. Finally, we build on these insights to outline best practices for crowdsourcing implicit measures in advertising and media research.

中文翻译：

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众包内隐联想测验：局限性和最佳实践

摘要尽管在媒体和广告研究中使用众包的在线面板收集行为数据在媒体和广告研究中很普遍，但直到最近，软件的发展才使研究人员能够轻松地在线收集隐式度量。出于近期MTurk数据质量下降和缺乏使用众包样本检查内隐联想测验的文献的影响，我们调查了八项使用各种样本（Mturk，在线本科生和本科生行为实验室）进行的IAT研究的横断面数据）。我们记录了参与者的注意力不集中，非天真和众包样本与传统样本之间缺乏动力的相对比率，并证明了这些威胁对IAT结果可靠性和有效性的影响。最后，