Blending Probability and Nonprobability Samples with Applications to a Survey of Military Caregivers
Blending Probability and Nonprobability Samples with Applications to a Survey of Military Caregivers
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将概率和非概率样本与军事护理人员调查的应用相混合
DOI：
10.1093/jssam/smaa037
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发表时间：
2020
影响因子：
2.1
通讯作者：
R. Ramchand
中科院分区：
文献类型：
--
作者：
Michael W Robbins;B. Ghosh;R. Ramchand
概率样本是提供可推广到更大总体的推论的首选方法。然而，在许多情况下，这种方法不太可能产生足够大的样本量来产生准确的推论。我们这里的目标是通过将概率样本与非概率样本组合(或混合)来提高从概率样本进行推断的效率，非概率样本(本身)潜在地充满了选择偏差，这将损害结果的普适性。我们开发了可用于此目的的新的统计加权方法。具体地说，我们区分了可用于使两个样本分别代表总体的权重(不相交混合)和仅使组合样本代表的权重(同时混合)。我们的重点是使用倾向性分数构建的权重，但也考虑了校准权重。我们包括模拟研究，这些研究表明，在结果与用于对齐样本的辅助变量强烈相关的情况下，方便样本提供的精度增益较低。展览的动机是对军事护理员的调查；我们的兴趣集中在2001年9月11日之后服役的受伤、生病或受伤的美国军人和退伍军人的无偿护理者。我们的工作不仅用来说明混合的正确执行，而且还用来警告读者它的危险，因为当假设有效时，引用非概率样本可能不会在精度上产生实质性的改进，并且在假设不成立的情况下可能会引起偏见。
Probability samples are the preferred method for providing inferences that are generalizable to a larger population. However, in many cases, this approach is unlikely to yield a sample size large enough to produce precise inferences. Our goal here is to improve the efficiency of inferences from a probability sample by combining (or blending) it with a nonprobability sample, which is (by itself) potentially fraught with selection biases that would compromise the generalizability of results. We develop novel methods of statistical weighting that may be used for this purpose. Specifically, we make a distinction between weights that can be used to make the two samples representative of the population individually (disjoint blending) and those that make only the combined sample representative (simultaneous blending). Our focus is on weights constructed using propensity scores, but consideration is also given to calibration weighting. We include simulation studies that, among other illustrations, show the gain in precision provided by the convenience sample is lower in circumstances where the outcome is strongly related to the auxiliary variables used to align the samples. Motivating the exposition is a survey of military caregivers; our interest is focused on unpaid caregivers of wounded, ill, or injured US servicemembers and veterans who served following September 11, 2001. Our work serves not only to illustrate the proper execution of blending but also to caution the reader with respect to its dangers, as invoking a nonprobability sample may not yield substantial improvements in precision when assumptions are valid and may induce biases in the event that they are not.
影响因子：
5
作者：
Brookhart, M. Alan;Schneeweiss, Sebastian;Sturmer, Til
通讯作者：
Sturmer, Til