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 a 2016 U.S. Presidential Election poll.
翻译:测量权重使研究人员能够利用测量的人口变量,用测量的人口变量,对调查样本中的偏差进行单位无反应或方便抽样,从而计算出调查的偏差。不幸的是,在实践中,无法知道估计调查权重是否足以减轻对未观察到的混淆者或加权中使用的不正确功能形式的偏差的关切。在下一份文件中,我们建议进行两项敏感性分析,以排除重要的共差:(1) 对部分观察到的共差者进行敏感性分析(即对调查样本中测得的变量,而不是目标人口),(2) 对完全未观察到的混淆者进行敏感性分析(即在调查中或目标人群中未测量的变量)。我们提供了此类混结者潜在偏差的图形和数字摘要,并采用了基准方法,使研究人员能够从数量上解释其结果的敏感性。我们用2016年的美国总统选举投票来展示我们提议的敏感性分析。