论文标题
调查权重的灵敏度分析
Sensitivity Analysis for Survey Weights
论文作者
论文摘要
调查加权使研究人员使用测量的人口协变量，由于单位无响应或便利性抽样，研究人员可以考虑调查样品的偏差。不幸的是，实际上，由于未观察到的混杂因素或加权中使用的不正确功能形式，估计的调查权重是否足以减轻对偏见的担忧。在下文中，我们提出了两个敏感性分析，以排除重要协变量：（1）对部分观察到的混杂因素进行敏感性分析（即，在整个调查样本中测量的变量，但不是目标群体，但不是完全未观察到的混杂因素（即，都无法测量的一个人群或目标人群）的敏感性分析（2）敏感性分析。我们提供了这些混杂因素产生的潜在偏差的图形和数值摘要，并引入了一种基准测试方法，使研究人员可以定量地理解其结果的敏感性。我们使用2020年美国总统选举民意调查的州级国家级别的敏感性分析证明了我们提出的敏感性分析。
Survey weighting allows researchers to account for bias in survey samples, due to unit nonresponse or convenience sampling, using measured demographic covariates. Unfortunately, in practice, it is impossible to know whether the estimated survey weights are sufficient to alleviate concerns about bias due to unobserved confounders or incorrect functional forms used in weighting. In the following paper, we propose two sensitivity analyses for the exclusion of important covariates: (1) a sensitivity analysis for partially observed confounders (i.e., variables measured across the survey sample, but not the target population), and (2) a sensitivity analysis for fully unobserved confounders (i.e., variables not measured in either the survey or the target population). We provide graphical and numerical summaries of the potential bias that arises from such confounders, and introduce a benchmarking approach that allows researchers to quantitatively reason about the sensitivity of their results. We demonstrate our proposed sensitivity analyses using state-level 2020 U.S. Presidential Election polls.
