Analysis of non-probability survey samples requires auxiliary information at the population level. Such information may also be obtained from an existing probability survey sample from the same finite population. Mass imputation has been used in practice for combining non-probability and probability survey samples and making inferences on the parameters of interest using the information collected only in the non-probability sample for the study variables. Under the assumption that the conditional mean function from the non-probability sample can be transported to the probability sample, we establish the consistency of the mass imputation estimator and derive its asymptotic variance formula. Variance estimators are developed using either linearization or bootstrap. Finite sample performances of the mass imputation estimator are investigated through simulation studies. We also address important practical issues of the method through the analysis of a real-world non-probability survey sample collected by the Pew Research Centre.

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通过大量插补结合非概率和概率调查样本

非概率调查样本的分析需要总体层面的辅助信息。此类信息也可以从来自相同有限总体的现有概率调查样本中获得。大规模插补已在实践中用于组合非概率和概率调查样本，并使用仅在研究变量的非概率样本中收集的信息对感兴趣的参数进行推断。在非概率样本的条件均值函数可以传递到概率样本的假设下，建立质量插补估计量的一致性并推导出其渐近方差公式。方差估计器是使用线性化或引导程序开发的。通过模拟研究研究了质量估算估计器的有限样本性能。我们还通过分析皮尤研究中心收集的真实世界非概率调查样本来解决该方法的重要实际问题。