Cited by 1,739Mobile Crowdsensing and CrowdsourcingSurvey Methodology and NonresponsePsychometric Methodologies and Testing

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Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants

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Hauser, David J., Schwarz, Norbert·2016·Behavior Research Methods

DOI 10.3758/s13428-015-0578-z

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Abstract

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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G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences
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Authors: Hauser, D. J., & Schwarz, N.
Affiliations: University of Michigan (US)
University of Southern California (US)
Corresponding author: Hauser, David J.
ORCID: Hauser, David J.
Schwarz, Norbert
Venue: Behavior Research Methods
Publisher: Springer Science and Business Media LLC
Published: 2015-03-12
Volume: 48
Issue: 1
Pages: 400-407
ISSN: 1554-351X (ISSN-L)
1554-3528
DOI: 10.3758/s13428-015-0578-z
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

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Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants

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