Evidence map›Paper›PMID 32135574›Full record

ArticleAddiction (Abingdon, England)2020

Mechanical Turk data collection in addiction research: utility, concerns and best practices.

Alexandra M Mellis, Warren K Bickel

Abstract read
In one paragraph

Article in Addiction (Abingdon, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

73 citing papers in PubMed.

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  18. The subjective value of social context in people who use cannabis.Experimental and clinical psychopharmacology · 2024
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13 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Alexandra M MellisAddiction Recovery Research Center, Fralin Biomedical Research Institute at VTC, VA, USA.
Warren K BickelAddiction Recovery Research Center, Fralin Biomedical Research Institute at VTC, VA, USA.ORCID 0000-0002-1048-7372

Funding

Self-Control Improvement Intervention (SCII): Improving Abstinence in SmokersR01DA034755 · NIDA · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI BICKEL, WARREN K · 2013 to 2017
$3.6M
Delay discounting as a target for self-regulation in prediabetesUH2DK109543 · NIDDK · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI BICKEL, WARREN K, EPSTEIN, LEONARD H · 2015 to 2017
$2.4M
Delay discounting as a target for self-regulation in prediabetesUH3DK109543 · NIDDK · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI BICKEL, WARREN K, EPSTEIN, LEONARD H · 2018 to 2019
$2.0M
NIDA NIH HHS R01 DA034755NIDDK NIH HHS UH2 DK109543NIDDK NIH HHS UH3 DK109543
6 · The paper itself

Abstract

aimsAmazon Mechanical Turk (MTurk) provides a crowdsourcing platform for the engagement of potential research participants with data collection instruments. This review (1) provides an introduction to the mechanics and validity of MTurk research; (2) gives examples of MTurk research; and (3) discusses current limitations and best practices in MTurk research.

methodsWe review four use cases of MTurk for research relevant to addictions: (1) the development of novel measures, (2) testing interventions, (3) the collection of longitudinal use data to determine the feasibility of longer-term studies of substance use and (4) the completion of large batteries of assessments to characterize the relationships between measured constructs. We review concerns with the platform, ways of mitigating these and important information to include when presenting findings.

resultsMTurk has proved to be a useful source of data for behavioral science more broadly, with specific applications to addiction science. However, it is still not appropriate for all use cases, such as population-level inference. To live up to the potential of highly transparent, reproducible science from MTurk, researchers should clearly report inclusion/exclusion criteria, data quality checks and reasons for excluding collected data, how and when data were collected and both targeted and actual participant compensation.

conclusionsAlthough on-line survey research is not a substitute for random sampling or clinical recruitment, the Mechanical Turk community of both participants and researchers has developed multiple tools to promote data quality, fairness and rigor. Overall, Mechanical Turk has provided a useful source of convenience samples despite its limitations and has demonstrated utility in the engagement of relevant groups for addiction science.

Indexed as

Behavior, AddictiveBehavioral ResearchCrowdsourcingData AccuracyData CollectionHumansPatient SelectionAddictionAmazon Mechanical Turkcrowdsourcingmethodson-linesurvey research

Identifiers

PMID32135574
PMCPMC7483427

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.