在全国的mHealth随机对照试验中检测欺骗和确保数据完整性:工厂设计调查研究
Krista M Kezbers1, Michael C Robertson1,2, Emily T Hébert1,2
1Tobacco Settlement Endowment Trust Health Promotion Research Center, Stephenson Cancer Center, University of Oklahoma Health Sciences, Oklahoma City, OK, United States.
Journal of medical Internet research
|January 28, 2025
概括
在远程试验中实施欺骗检测标记了26%的预先选人员的潜在欺诈行为. 警对于维护数据完整性和在线行为研究中的参与者安全至关重要.
科学领域:
- 行为科学 行为科学
- 数字健康数字健康
- 研究方法研究方法研究方法学
背景情况:
- 在社会和行为研究中,远程招聘正在增加.
- 欺骗性的注册尝试 (例如,机器人,假个人资料) 带来了挑战.
- 需要最佳实践来确保在线研究中的数据完整性.
研究的目的:
- 制定和实施欺诈检测程序.
- 缓解在远程试验中欺骗性招生造成的负面影响.
- 提高在线研究中的数据质量和参与者安全.
主要方法:
- 远程进行了一项32组的因数设计研究.
- 招聘使用了Facebook广告,在线预选器 (REDCap) 和电话.
- 实施了12个步骤的检查清单,以检测欺骗性的入学尝试.
主要成果:
- 26%的预先选被标记为潜在的欺骗性.
- 常见的标志包括多次提交和无效地址.
- 报名后的检查发现,只有很小一部分参与者有错误的数据.
结论:
- 欺诈检测程序标记了大量潜在的欺骗性招生.
- 警对于远程行为研究的完整性至关重要.
- 系统的检测方法支持研究管理,数据质量和安全.
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