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Reputation as a sufficient condition for data quality on Amazon Mechanical Turk
Open accessAbstract
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.
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“Data obtained from MTurk has been demonstrated to be valid and reliable when appropriate selection criteria and attention checks are used (Casler, Bickel, & Hackett, 2013; Peer, Vosgerau, & Acquisti, 2014), as was the case in this study.”
From The development and evaluation of a brief form of the Normative Male Alexithymia Scale (NMAS-BF). (2019) · cites this paper as Peer, Vosgerau, & Acquisti, 2014
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In the citing paper
“Most of the sites we reviewed have supervision and reputation mechanisms designed to measure the quality of submitted work, and to allow researchers to invite workers with high reputation (similar to MTurk's”
From Beyond the Turk: Alternative platforms for crowdsourcing behavioral research (2017) · cites this paper as Peer et al., 2014
In the citing paper
“Amazon Mechanical Turk is an online sample recruiting platform that, although not nationally representative, yields high data quality through the use of attention checks 44 and worker reputation scores 45 .”
From Understanding, explaining, and utilizing medical artificial intelligence (2021) · cites this paper as [45]
In the citing paper
“Following prior research, we selected participants with an approval rating above 95% on Amazon Mechanical Turk 45 .”
From Understanding, explaining, and utilizing medical artificial intelligence (2021) · cites this paper as [45]
In the citing paper
“which showed that ACQs have low diagnostic ability when data quality is already high.”
From Beyond the Turk: Alternative platforms for crowdsourcing behavioral research (2017) · cites this paper as Peer et al., (2014)
In the citing paper
“they are reliable and validated scales, and b) they have been previously used with success to measure data quality on MTurk (Peer et al., 2014).”
From Beyond the Turk: Alternative platforms for crowdsourcing behavioral research (2017) · cites this paper as Peer et al., 2014
In the citing paper
“they are reliable and validated scales, and b) they have been previously used with success to measure data quality on MTurk (Peer et al., 2014).”
From Beyond the Turk: Alternative platforms for crowdsourcing behavioral research (2017) · cites this paper as Peer et al., 2014
In the citing paper
“Using two attention-check questions (Peer et al., 2014), we checked whether participants read and paid attention to our instructions.”
From Beyond the Turk: Alternative platforms for crowdsourcing behavioral research (2017) · cites this paper as Peer et al., 2014
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Füllbrunn, S., Delsen, L., & Vyrastekova, J. (2019) · Contributions to economics · doi:10.1007/978-3-030-30044-9_7 · Cited by 9
Metadata
- Authors
- Peer, E., Vosgerau, J., & Acquisti, A.
- Affiliations
- Tilburg University (NL)
- Carnegie Mellon University (US)
- Venue
- Behavior Research Methods
- Published
- 2013-12-20
- Volume
- 46
- Issue
- 4
- Pages
- 1023-1031
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W2105753174
- Cited by
- 1,618
- References
- 18
- Citations by year
- 2026: 68 · 2025: 96 · 2024: 128 · 2023: 167 · 2022: 175 · 2021: 212 · 2020: 210 · 2019: 147 · 2018: 119 · 2017: 132 · 2016: 91 · 2015: 49 · 2014: 17 · 2013: 1
- FWCI
- 79.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Behavior Research Methods (publishedVersion)
- Research portal (Tilburg University) (publishedVersion)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- SSRN Electronic Journal (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Archivio istituzionale della Ricerca - Bocconi (submittedVersion)
- KiltHub Repository
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- data quality, Amazon Mechanical Turk, worker reputation, crowdsourcing, selection bias, labor productivity, participant recruitment
- MeSH terms
- Data Collection, Humans, Research Design, Patient Selection, Internet, Behavioral Research, Crowdsourcing
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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