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Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants
Open accessAbstract
Participant attentiveness is a concern for many researchers using Amazon's Mechanical Turk (MTurk). Although studies comparing the attentiveness of participants on MTurk versus traditional subject pool samples have provided mixed support for this concern, attention check questions and other methods of ensuring participant attention have become prolific in MTurk studies. Because MTurk is a population that learns, we hypothesized that MTurkers would be more attentive to instructions than are traditional subject pool samples. In three online studies, participants from MTurk and collegiate populations participated in a task that included a measure of attentiveness to instructions (an instructional manipulation check: IMC). In all studies, MTurkers were more attentive to the instructions than were college students, even on novel IMCs (Studies 2 and 3), and MTurkers showed larger effects in response to a minute text manipulation. These results have implications for the sustainable use of MTurk samples for social science research and for the conclusions drawn from research with MTurk and college subject pool samples.
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“In crowd-sourced settings such as MTurk, respondents are often incentivized to respond as attentively, compliantly, and quickly as possible to both open higher-paying opportunities and prevent being rejected from future employment [90, 100].”
From Not so terrifying after all? A set of failed replications of the mortality salience effects of Terror Management Theory (2023) · cites this paper as [100]
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“Comparable attention checks have been shown to improve the quality of data obtained through web-based surveys above that of classic paper and pencil questionnaires [57].”
From Dispositional factors in the explanation of symptoms of depression, anxiety, health anxiety and COVID-19 Phobia (2024) · cites this paper as [57]
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Metadata
- Authors
- Hauser, D. J., & Schwarz, N.
- Affiliations
- University of Michigan (US)
- University of Southern California (US)
- Corresponding author
- Hauser, David J.
- Venue
- Behavior Research Methods
- Published
- 2015-03-12
- Volume
- 48
- Issue
- 1
- Pages
- 400-407
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W2076387137
- Cited by
- 1,739
- References
- 23
- Citations by year
- 2026: 60 · 2025: 104 · 2024: 145 · 2023: 180 · 2022: 209 · 2021: 249 · 2020: 248 · 2019: 176 · 2018: 160 · 2017: 122 · 2016: 64 · 2015: 19
- FWCI
- 184.66
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behavior Research Methods (publishedVersion)
- PubMed (publishedVersion)
- 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
- Amazon Mechanical Turk, university students
- MeSH terms
- Attention, Data Collection, Female, Humans, Learning, Male, Surveys and Questionnaires, Social Sciences, Internet
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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