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

E. Peer, J. Vosgerau, A. Acquisti

Research output: Contribution to journalArticleScientificpeer-review

562 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)1023-1031
JournalBehavior Research Methods
Volume46
Issue number4
Early online date20 Dec 2013
DOIs
Publication statusPublished - Dec 2014

Keywords

  • Online research
  • Amazon Mechanical Turk
  • data quality
  • reputation

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