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Conducting Clinical Research Using Crowdsourced Convenience Samples
Abstract
Crowdsourcing has had a dramatic impact on the speed and scale at which scientific research can be conducted. Clinical scientists have particularly benefited from readily available research study participants and streamlined recruiting and payment systems afforded by Amazon Mechanical Turk (MTurk), a popular labor market for crowdsourcing workers. MTurk has been used in this capacity for more than five years. The popularity and novelty of the platform have spurred numerous methodological investigations, making it the most studied nonprobability sample available to researchers. This article summarizes what is known about MTurk sample composition and data quality with an emphasis on findings relevant to clinical psychological research. It then addresses methodological issues with using MTurk--many of which are common to other nonprobability samples but unfamiliar to clinical science researchers--and suggests concrete steps to avoid these issues or minimize their impact.
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“Chandler and Shapiro (2016), including empirical work by Shapiro and colleagues (2013), demonstrate that MTurk-recruited research study participants are similarly honest, attentive, and reliable compared to non-MTurk samples, and that MTurk is particularly well suited to reaching populations with social anxiety symptoms.”
From A Randomized Trial of Brief Online Interventions to Facilitate Treatment Seeking for Social Anxiety. (2022) · cites this paper as Chandler and Shapiro, 2016
In the citing paper
“However, as noted, research reviewed by Chandler and Shapiro (2016), including empirical work by Shapiro and colleagues (2013), illustrates that MTurk workers are comparably honest, attentive, and reliable compared to non-MTurk samples, and that MTurk is well suited to reaching populations with clinically relevant symptoms.”
From A Randomized Trial of Brief Online Interventions to Facilitate Treatment Seeking for Social Anxiety. (2022) · cites this paper as Chandler and Shapiro (2016)
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Metadata
- Authors
- Chandler, J., & Shapiro, D.
- Affiliations
- Mathematica Policy Research (US)
- University of Michigan (US)
- ORCID
- Chandler, Jesse
- Venue
- Annual Review of Clinical Psychology
- Publisher
- Annual Reviews
- Published
- 2016-03-28
- Volume
- 12
- Issue
- 1
- Pages
- 53-81
- ISSN
- 1548-5943 (ISSN-L)
- 1548-5951
- OpenAlex
- W2126493218
- Cited by
- 1,072
- References
- 177
- Citations by year
- 2026: 27 · 2025: 70 · 2024: 105 · 2023: 103 · 2022: 150 · 2021: 168 · 2020: 122 · 2019: 115 · 2018: 111 · 2017: 77 · 2016: 24
- FWCI
- 17.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annual Review of Clinical Psychology (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Keywords
- crowdsourcing, Amazon Mechanical Turk, convenience samples, non-probability sampling, sample composition, data quality, participant recruitment, digital labour markets
- MeSH terms
- Humans, Biomedical Research, Crowdsourcing
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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