ArticleThe International journal of eating disorders2022
Ethical concerns arising from recruiting workers from Amazon's Mechanical Turk as research participants: Commentary on Burnette et al. (2021).
Article in The International journal of eating disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Parental Overvaluation of Child Weight/Shape Is Associated With Disordered Eating in Children Beyond Associations With Parental Internalized Weight Bias.Journal of the Academy of Nutrition and Dietetics · 2025Article
- Brain-computer interfaces, disability, and the stigma of refusal: A factorial vignette study.Public understanding of science (Bristol, England) · 2023Article
- Research Ethics in the Age of Digital Platforms.Science and engineering ethics · 2023Article
- Balancing data integrity concerns with ethical considerations in online research.The International journal of eating disorders · 2022Article
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Abstract
In this commentary, we respond to Burnette et al.'s (2021) paper, which gives significant practical recommendations to improve data quality and validity while gathering data via Amazon's Mechanical Turk (MTurk). We argue that it is also important to acknowledge and review the specific ethical issues that might arise when recruiting MTurk workers as participants. We particularly raise three main ethical concerns that need to be addressed when recruiting research participants from participant recruitment platforms: participants' economic vulnerability, participants' sensitivity, and power dynamics between participants and researchers. We elaborate on these issues by discussing the ways in which they may appear and be responded to. We conclude that considering the ethical aspects of data collection and the potential impacts of data collection on those involved would complement Burnette et al.'s recommendations. Consequently, data collection processes should be transparent as well, in addition to data screening processes.
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