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Reputation as a sufficient condition for data quality on Amazon Mechanical Turk

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journal contribution
posted on 22.10.2021, 19:13 by Eyal Peer, Joachim Vosgerau, Alessandro AcquistiAlessandro Acquisti
Data quality is one of the major concerns of using crowdsourcing websites such as Amazon Mechanical Turk (MTurk) to recruit participants for online behavioral studies. We compared two methods for ensuring data quality on MTurk: attention check questions (ACQs) and restricting participation to MTurk workers with high reputation (above 95% approval ratings). In Experiment 1, we found that high-reputation workers rarely failed ACQs and provided higher-quality data than did low-reputation workers; ACQs improved data quality only for low-reputation workers, and only in some cases. Experiment 2 corroborated these findings and also showed that more productive high-reputation workers produce the highest-quality data. We concluded that sampling high-reputation workers can ensure high-quality data without having to resort to using ACQs, which may lead to selection bias if participants who fail ACQs are excluded post-hoc.


Publisher Statement

This is the author's version of the following article: Peer, E., Vosgerau, J. & Acquisti, A. Reputation as a sufficient condition for data quality on Amazon Mechanical Turk. Behav Res 46, 1023–1031 (2014). It has been published in final form at This article may be used for non-commercial purposes only.