高质量的在线数据收集负担在于研究人员,而不是招聘平台
Christine Cuskley1, Justin Sulik2
1Language Evolution, Acquisition, and Development Group, Newcastle University.
概括
批评一个研究的研究.
科学领域:
- 心理学科学 心理学科学
- 在线研究方法 在线研究方法
背景情况:
- 最近的一项研究报告说,亚马逊的机械土耳其人 (MTurk) 的参与者有效率低 (2.6%).
- 该研究对MTurk不足的说法得到了广泛传播.
- 这篇评论解决了有关MTurk在研究中的实用性的误解.
研究的目的:
- 为了纠正有关MTurk数据质量的记录.
- 建议对进行高质量的在线研究提出修订后的观点.
- 强调研究人员对数据完整性的责任.
主要方法:
- 对影响参与者有效性的数据收集方法的分析.
- 关于有效的MTurk招聘策略的现有文献的审查.
- 讨论平台特定的约束和研究人员的专业知识.
主要成果:
- 原始研究的低有效率源于其特定的数据收集方法.
- MTurk对于研究的适用性取决于研究设计和研究人员的专业知识.
- 有效的在线研究需要仔细考虑所选择的平台的局限性.
结论:
- 在适当使用时,MTurk可以产生高质量的数据.
- 研究人员必须具备特定的专业知识,以优化在线数据收集.
- 确保数据质量是研究人员固有的责任,无论平台如何.
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