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在基于网络的调查中评估和改善数据完整性:在COVID-19研究中比较欺诈检测系统
Stephen Bonett1, Willey Lin1, Patrina Sexton Topper1
1School of Nursing, University of Pennsylvania, Philadelphia, PA, United States.
JMIR formative research
|January 12, 2024
概括
网络调查的多层欺诈检测系统显示与专有工具的公平协议,影响了样本组成和疫苗信心分布. 量身定制的,由人类审查的方法可以提高数据完整性.
科学领域:
- 公共卫生 公共卫生
- 调查方法 调查方法
- 数据科学数据科学数据科学
背景情况:
- 基于网络的调查提高了参与度,但面临着机器人输入和重复提交等数据质量威胁.
- 专有欺诈检测工具缺乏方法和有效性的透明度.
- 需要强大的,可重复的方法来确保网络调查数据的完整性.
研究的目的:
- 描述COVID-19网络调查的多层欺诈检测系统.
- 检查定制系统和专有系统 (Qualtrics) 之间的协议.
- 比较每个欺诈检测方法的研究样本.
主要方法:
- 一项跨部门网络调查 (PhillyCEAL常见调查) 评估了COVID-19的影响.
- 将自定义的多层欺诈检测策略 (自动验证+人体审查) 与Qualtrics专有系统进行了比较.
- 分析了使用描述性统计和分类表的共识;根据种族/种族对疫苗信心的评估影响.
主要成果:
- 关税系统确定了40.60%的有效病例; 夸尔特里克斯确定了55.21%.
- 方法之间的一致性是"公平"到"最小" (κ=0.25).
- 欺诈检测方法的选择影响了按种族/民族群体的疫苗信心分布.
结论:
- 欺诈检测方法的选择显著影响研究样本的组成.
- 建议在未来的调查中采用多层次的方法,将自动检测与适合上下文的人类审查相结合.
- 确保数据完整性对于最大限度地提高基于网络的调查研究价值至关重要.
关键词:
在 COVID-19 疫情中,费城 费城 费城 费城 是一个这是一个横截面的横截面.数据数据的数据数据的数据.数据隐私 隐私数据 隐私数据数据保护数据的保护.数据质量数据质量数据质量数据验证数据的验证.欺诈 欺诈 欺诈 欺诈 欺诈 欺诈发现欺诈 发现欺诈信息安全信息安全.隐私 隐私 隐私 隐私 隐私 隐私安全的安全的安全的安全的安全.调查调查调查调查调查调查调查调查调查方法调查方法研究方法.基于网络的基于网络的基于网络的调查调查.更多相关视频
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