缓解在线调查中的欺诈:一种方法学方法
Karen M Towne1,2,3, Barbara Polivka1
1University of Kansas School of Nursing, USA.
Western journal of nursing research
|March 4, 2026
概括
本研究详细介绍了通过识别和减轻欺诈性回复来提高在线调查数据质量的方法. 实施八步协议可以主动阻止和检测研究调查中的可疑数据.
科学领域:
- 数据科学数据科学数据科学
- 研究方法研究方法研究方法学
- 调查设计 调查设计
背景情况:
- 在线调查提供了高效的数据收集,但面临着诸如欺诈性回复等挑战,特别是在使用众包平台时.
- 像Research Match和Prolific这样的平台提供了独特的招聘优势和陷.
研究的目的:
- 描述增强在线REDCap调查的策略,以识别和减轻欺诈和可疑的响应.
- 提出一种方法来加强在线数据收集,以防数据完整性受到威胁.
主要方法:
- 采用了双方向的方法,重点关注最初的设计局限性和基于证据的重新设计.
- 策略包括实施诈骗警报功能,修改研究设计,改进调查结构,并考虑众包平台的具体情况.
主要成果:
- 在与机构审查委员会和REDCap专家的合作下,开发并实施了一个八步数据清理协议.
- 多个数据清理步骤确定了相同的欺诈记录,表明欺诈数据通常会显示出几个可疑指标.
结论:
- 在线数据收集提供了机会,同时存在数据质量受损和资源浪费的风险.
- 积极主动,基于证据的协议对于阻止和早期检测欺诈结果至关重要,以确保数据完整性.
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