2022
DOI: 10.1002/eat.23675
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Eating disorder related research using Amazon Mechanical Turk (MTurk): Friend or foe?: Commentary on Burnette et al. (2021)

Abstract: Burnette et al. reported a study that they sought to undertake to validate common eating disorder questionnaires in sexual and gender minorities. The researchers took advantage of the online recruitment platform Amazon Mechanical Turk (MTurk). Contrary to their expectations, the study proved not feasible due to invalid answering.

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Cited by 7 publications
(3 citation statements)
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“…As we did not control where, when and how participants filled in the vignette task and the questionnaires, they might have completed the study in a noisy and distracting environment. To reduce the risk that inattentive reading and incorrect answers would affect the validity of our data, we followed current guidelines for online research and included several check questions [59]. At the same time, the online setting potentially encouraged participants to disclose more personal information and to provide less socially desirable answers due to a greater level of anonymity and the possibility of completing the study in a comfortable environment [60].…”
Section: Limitations and Future Researchmentioning
confidence: 99%
“…As we did not control where, when and how participants filled in the vignette task and the questionnaires, they might have completed the study in a noisy and distracting environment. To reduce the risk that inattentive reading and incorrect answers would affect the validity of our data, we followed current guidelines for online research and included several check questions [59]. At the same time, the online setting potentially encouraged participants to disclose more personal information and to provide less socially desirable answers due to a greater level of anonymity and the possibility of completing the study in a comfortable environment [60].…”
Section: Limitations and Future Researchmentioning
confidence: 99%
“…However, with the increasing use of web-based research and advances in technology, researchers continue to point to challenges in data integrity, particularly in quantitative data collection [4,[6][7][8][9][10][11][12]. Known issues with fully web-based recruitment and data collection include data farming, bots, mischievous responders (responders who intentionally mislead researchers), low data quality when tasks are completed quickly, and bypassing IP address restrictions (which would allow a participant to complete a survey multiple times) [4,[6][7][8][9][10][11][12].…”
Section: Introduction and Literature Reviewmentioning
confidence: 99%
“…However, with the increasing use of web-based research and advances in technology, researchers continue to point to challenges in data integrity, particularly in quantitative data collection [4,[6][7][8][9][10][11][12]. Known issues with fully web-based recruitment and data collection include data farming, bots, mischievous responders (responders who intentionally mislead researchers), low data quality when tasks are completed quickly, and bypassing IP address restrictions (which would allow a participant to complete a survey multiple times) [4,[6][7][8][9][10][11][12]. To combat the challenges of web-based recruitment and quantitative data collection, researchers have aimed to describe best practices and protocols in survey development, implementation, and data cleaning to promote validity in web-based quantitative data collection [4,[6][7][8][9][10][11][12].…”
Section: Introduction and Literature Reviewmentioning
confidence: 99%