Publication details
Amazon Mechanical Turk in Sport Science research: Opportunities and pitfalls of crowdsourced data collection
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Conference abstract |
| MU Faculty or unit | |
| Citation | |
| Description | Background: Amazon Mechanical Turk (MTurk) is increasingly used in sports science, exercise psychology, physical activity research, and sport management. Currently, there is no comprehensive overview describing how MTurk has been applied in these fields or which methodological aspects are reported. Mapping existing applications may clarify how MTurk has been used in sport-related research contexts. Methods: A targeted narrative review was conducted in the Web of Science (14 November 2025), yielding 126 records. Studies were screened using a relevance-based extraction procedure grounded in the Weight of Evidence framework, and those meeting a predefined 70% relevance threshold were retained. Thirty-six studies met this criterion, of which six lacked accessible full texts; therefore, 30 studies were included in the final analysis. Data were extracted using a structured template and synthesised descriptively. Results: The included studies demonstrated that MTurk has been applied in four areas: physical activity and exercise psychology, digital data processing tasks, sport consumer and fan behaviour, and public perceptions of sport-related health risks. MTurk enabled rapid recruitment, the collection of large samples, and the delivery of surveys, experiments, and crowdsourced annotations. Reported procedures commonly involved prescreening, approval thresholds, attention checks, and compensation details. Several studies also noted data-quality risks, including sampling bias, inattention, and fraudulent responding. Conclusions: MTurk supports a wide range of sport-related research applications, from physical activity and exercise psychology to digital data processing, consumer behaviour, and public-health perceptions. At the same time, the extracted studies highlight recurring methodological risks—especially sampling bias, inattentive or fraudulent responding, and variability in annotation quality—that require careful management. Understanding these patterns can help researchers design more robust MTurk protocols and make more informed decisions about the platform’s suitability for future sports-science studies. |
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