数据或欺骗:在线定性研究中的假冒参与者
Paul Sharp1,2, Nina Gao2, Matthew Sha2
1School of Health Sciences, University of New South Wales, Sydney, NSW, Australia.
Qualitative health research
|February 28, 2026
概括
在线定性研究面临着"骗子参与者"寻求荣誉的挑战,危及数据完整性和增加行政负担. 需要制定战略来检测和阻止这些人,同时确保包容性参与者招聘.
科学领域:
- 定性研究方法 定性研究方法
- 在线数据收集在线数据收集
- 研究伦理学研究伦理学
背景情况:
- 在COVID-19大流行期间,定性研究中的在线招聘和数据收集激增,提供了成本和时间效率.
- 在线方法的兴起带来了风险,特别是受研究费用激励的"骗子参与者".
- 这些骗子会造成行政负担,并威胁到数据完整性和公平抽样.
研究的目的:
- 探索与在线定性研究中识别和管理"骗子参与者"相关的挑战和策略.
- 提供加拿大一项关于男性心理健康和同行支持的光声研究的见解.
- 解决假冒参与者对数据完整性,研究人员工作量和机构安全的影响.
主要方法:
- 来自加拿大在线光声研究的经验分析.
- 挑战的主题探索,包括检测伪造者,真实性选和研究人员压力.
- 对参与者查的技术工具,人类策略和伦理考虑的审查.
主要成果:
- "伪造者参与者"对数据完整性构成重大风险,并增加在线定性研究中的项目成本.
- 有效的检测和威需要技术工具和以人为中心的战略的结合.
- 为了避免排除符合条件的个人,平衡参与者真实性选与包容性至关重要.
结论:
- 假冒参与者的普遍性需要强有力的策略来检测,威和研究人员支持.
- 大学系统需要升级,以改善针对人工智能产生的诈骗的安全性和风险管理.
- 解决研究人员的压力和疲劳对于保持在线定性研究的质量和伦理行为至关重要.
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