使用数据质量框架评估混合数据的数据质量
Jennifer D Parker1, Lisa B Mirel2, Phillip Lee3
1National Center for Health Statistics, Centers for Disease Control and Prevention, U.S. Department of Health and Human Services.
概括
应用美国联邦统计方法委员会 (FCSM) 框架对混合数据的数据质量评估是复杂的. 指导和理解权衡对于研究中有效的数据质量评估至关重要.
科学领域:
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
- 卫生研究 卫生研究 卫生研究
背景情况:
- 美国联邦统计方法委员会 (FCSM) 在2020年发布了数据质量框架.
- 这个框架将数据质量组织成11个维度,跨实用性,客观性和完整性领域.
- 实施该框架的最佳实践,特别是混合数据,需要进一步的文档.
研究的目的:
- 评估FCSM数据质量框架的应用,以评估混合数据.
- 通过使用现实世界的案例研究,识别数据质量评估中的挑战,缓解和权衡.
主要方法:
- 将FCSM数据质量框架应用于三项涉及混合数据的健康研究案例研究.
- 对每个维度进行数据质量评估,以确定威胁和缓解策略.
主要成果:
- 在实践中,数据质量评估比最初预期的要复杂得多.
- 个别数据质量维度的重要性取决于预期的数据使用.
- 评估中的主观性凸显了定量工具的潜在益处,尽管这些依赖于用例.
- 在不同的数据质量维度中存在共同的权衡和缓解策略.
结论:
- 专家指导和全面的文档对于有效实施FCSM框架至关重要.
- 需要采取细微的方法,认识到并非所有数据质量维度对每个应用程序都同样重要.
- 定量评估工具可以帮助解释结果,但必须根据特定的数据用途量身定制.
- 了解跨维的权衡和共同的缓解策略是有效管理数据质量的关键.
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