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

Eyal Peer*, Joachim Vosgerau, Alessandro Acquisti

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1224 Scopus citations

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 languageAmerican English
Pages (from-to)1023-1031
Number of pages9
JournalBehavior Research Methods
Volume46
Issue number4
DOIs
StatePublished - Dec 2014
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013, Psychonomic Society, Inc.

Keywords

  • Amazon Mechanical Turk
  • Data quality
  • Online research
  • Reputation

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