行为研究的方法考虑依赖于通过在线众包平台的响应时间结果
Patrick A McConnell1, Christian Finetto1, Kirstin-Friederike Heise2
1Integrative Neuromodulation and Recovery (iNR) Laboratory, Department of Health Sciences and Research, Medical University of South Carolina, 77 President Street, Charleston, SC, 29425, USA.
Scientific reports
|April 2, 2024
概括
在线众包数据收集面临技术问题和人为因素的偏见,影响时间敏感的结果. 研究人员可以通过实施硬件检查和参与者监测可靠数据来减轻这些挑战.
科学领域:
- 人与计算机的交互
- 心理学 心理学 心理学
- 数据科学数据科学数据科学
背景情况:
- 在线众包数据收集越来越多地用于研究.
- 具有时间敏感结果 (例如响应延迟) 的纵向研究特别容易受到数据质量问题的影响.
- 现有的方法往往忽略了引入偏差的技术和人为因素.
研究的目的:
- 识别和讨论在线众包数据收集的重大挑战,用于纵向,时间敏感的任务.
- 探索可能影响数据可靠性的技术和人为因素相关偏差.
- 提出可行的建议,以改善数据质量和减轻在线研究中的陷.
主要方法:
- 这篇视角论文综合了现有研究,并确定了关键挑战.
- 分析侧重于两种主要的偏见来源:技术缺陷和人为因素.
- 通过对最佳实践和建议干预措施的审查,探索潜在的解决方案.
主要成果:
- 确定了两个主要的偏见来源:技术问题 (低/可变的率) 和人为因素 (高参与者 attrition).
- 技术缺陷可能会扭曲时间敏感的测量,如响应延迟.
- 人类因素在纵向研究中显著影响参与者保留和数据完整性.
结论:
- 实施硬件加速和设置研究特定的率值可以解决技术偏差.
- 预先选参与者和持续监测硬件性能和参与度对于减轻人为因素至关重要.
- 研究人员必须意识到并积极应对这些挑战,以确保在线众包数据的质量和可靠性.
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