The DOI matches its Crossref record; 21 of the 23 references it cites were found in a registry or an index.
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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Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007) · Behavior Research Methods · doi:10.3758/bf03193146 · Cited by 65,971
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Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk
Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012) · Political Analysis · doi:10.1093/pan/mpr057 · Cited by 4,008
Running experiments on Amazon Mechanical Turk
Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010) · Judgment and Decision Making · doi:10.1017/s1930297500002205 · Cited by 3,767
Instructional manipulation checks: Detecting satisficing to increase statistical power
Oppenheimer, D. M., Meyvis, T., & Davidenko, N. (2009) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2009.03.009 · Cited by 3,493
Mental Accounting and Consumer Choice
Thaler, R. H. (2008) · Marketing Science · doi:10.1287/mksc.1070.0330 · Cited by 3,477
Conducting behavioral research on Amazon’s Mechanical Turk
Mason, W., & Suri, S. (2012) · Behavior Research Methods · doi:10.3758/s13428-011-0124-6 · Cited by 3,172
Data collection in a flat world: the strengths and weaknesses of Mechanical Turk samples
Goodman, J. K., Cryder, C. E., & Cheema, A. (2013) · Journal of Behavioral Decision Making · doi:10.1002/bdm.1753 · Cited by 2,242
Inside the Turk: Understanding Mechanical Turk as a Participant Pool
Paolacci, G., & Chandler, J. (2014) · Current Directions in Psychological Science · doi:10.1177/0963721414531598 · Cited by 2,136
Research works printed but not linked: 1
- Kapelner, A., & Chandler, D. (2010). Preventing satisficing in online surveys: A “kapcha” to ensure higher data quality. In Proceedings of CrowdConf 2010. Available at www.academia.edu/2788541/Preventing_Satisficing_in_Online_Surveys
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The Role of Personality in Trust in Public Policy Automation
Waggoner, P., & Kennedy, R. (2022) · Journal of Behavioral Data Science · doi:10.35566/v2n1/p4/ · Cited by 7
Bilingual Language Dominance and Code-switching Patterns
Olson, D. J. (2024) · Heritage Language Journal · doi:10.1163/15507076-bja10027 · Cited by 4
Mayor, E., & Lieb, R. (2024) · PLoS ONE · doi:10.1371/journal.pone.0299593
“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].”
Background
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
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