Addiction Recovery Research Center, Fralin Biomedical Research Institute at VTC, VA, USA.
Addiction. 2020 Oct;115(10):1960-1968. doi: 10.1111/add.15032. Epub 2020 Mar 24.
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.
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.
MTurk 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.
Although 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.
亚马逊 Mechanical Turk（MTurk）为潜在研究参与者提供了一个众包平台，用于使用数据收集工具进行研究。本综述（1）介绍了 MTurk 研究的机制和有效性；（2）提供了 MTurk 研究的实例；（3）讨论了 MTurk 研究中的当前限制和最佳实践。
我们回顾了 MTurk 在成瘾研究中四个用途的案例：（1）开发新的测量工具；（2）测试干预措施；（3）收集纵向使用数据，以确定更长期药物使用研究的可行性；（4）完成大量评估，以描述测量结构之间的关系。我们审查了对该平台的担忧，减轻这些担忧的方法以及在呈现研究结果时需要包含的重要信息。
MTurk 已被证明是行为科学（包括成瘾科学）的一种有用的数据来源。然而，它仍然不适用于所有用例，例如人群推断。为了充分发挥 MTurk 高度透明、可重复科学的潜力，研究人员应该清楚地报告纳入/排除标准、数据质量检查以及排除收集数据的原因、数据收集的方式和时间，以及目标和实际参与者的补偿。
尽管在线调查研究不能替代随机抽样或临床招募，但 MTurk 的参与者和研究人员社区已经开发出多种工具来提高数据质量、公平性和严谨性。总体而言，尽管存在局限性，但 Mechanical Turk 为方便样本提供了有用的来源，并在吸引成瘾科学相关群体方面展示了其实用性。
