AIMS Amazon 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. METHODS We 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. RESULTS MTurk has proven 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 was collected, and both targeted and actual participant compensation. CONCLUSIONS Although online survey research is not a substitute for random sampling or clinical recruitment, the MTurk community of both participants and researchers has developed multiple tools to promote data quality, fairness, and rigor. Overall, MTurk has provided a useful source of convenience samples despite its limitations and has demonstrated utility in the engagement of relevant groups for addiction science.

中文翻译：

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成瘾研究中的机械土耳其人数据收集：实用程序、关注点和最佳实践

AIMS Amazon Mechanical Turk (MTurk) 提供了一个众包平台，让潜在的研究参与者参与数据收集工具。本综述 (1) 介绍了 MTurk 研究的机制和有效性；（2）给出MTurk研究的例子；(3) 讨论 MTurk 研究的当前局限性和最佳实践。方法 我们回顾了 MTurk 用于成瘾研究的四个用例：1）新措施的开发，2）测试干预措施，3）收集纵向使用数据以确定物质使用长期研究的可行性，以及 4 ) 完成大量评估以表征测量结构之间的关系。我们审查对平台的担忧，缓解这些担忧的方法，以及在展示调查结果时要包含的重要信息。结果 MTurk 已被证明是更广泛的行为科学数据的有用来源，特别适用于成瘾科学。但是，它仍然不适用于所有用例，例如人口级别的推理。为了发挥 MTurk 高度透明、可重复科学的潜力，研究人员应明确报告纳入/排除标准、数据质量检查和排除收集数据的原因、收集数据的方式和时间，以及目标和实际参与者补偿。结论 虽然在线调查研究不能替代随机抽样或临床招募，但参与者和研究人员的 MTurk 社区已经开发了多种工具来提高数据质量、公平性和严谨性。全面的，