不代表性的大型调查大大高估了美国的疫苗接种量
Valerie C Bradley1, Shiro Kuriwaki2, Michael Isakov3
1Department of Statistics, University of Oxford, Oxford, UK.
Nature
|December 9, 2021
概括
大规模的调查可能会因为偏见而导致误导, 数据的质量, 不仅仅是数量, 对于准确的调查结果和了解公众对疫苗接种等主题的意见至关重要.
科学领域:
- 公共卫生
- 调查方法
- 数据科学
背景情况:
- 调查对于衡量公众意见和行为至关重要.
- 统计代表性是调查准确性的关键,需要尽量减少偏见.
- 大数据悖论强调了数据大小的增加会加大调查偏差.
研究的目的:
- 通过COVID-19疫苗接种估计来证明大数据悖论.
- 为了比较大规模调查的准确性与较小的,方法上健全的小组.
- 分析调查偏差对理解疫苗犹的影响.
主要方法:
- 来自Delphi-Facebook和人口普查家庭脉冲调查的第一剂COVID-19疫苗接种数据分析 (2021年1月至5月).
- 调查估计与疾病控制和预防中心的基准值进行比较.
- 使用最近的分析框架对调查错误的分解.
- 一个依据调查研究最佳实践的Axios-Ipsos在线小组的评估.
主要成果:
- 大规模调查 (Delphi-Facebook,人口普查家庭脉冲) 大大高估了COVID-19疫苗接种率.
- 尽管样本规模很大,但这些调查的估计结果不准确,错误率很小.
- 一个较小的调查 (Axios-Ipsos) 使用最佳实践产生了可靠的估计和不确定性量化.
结论:
- 在调查研究中,数据质量比数据量更为重要.
- 在不考虑偏差的情况下依赖大型数据集会导致数学上可证明的不准确性.
- 调查设计中的方法严谨对于可靠的论洞察至关重要.
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