Using proxy measures and other correlates of survey outcomes to adjust for non‐response: examples from multiple surveys
Using proxy measures and other correlates of survey outcomes to adjust for non‐response: examples from multiple surveys
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使用代理措施和调查结果的其他相关因素来调整无答复：多项调查的示例
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
T. Raghunathan
中科院分区：
文献类型：
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作者：
F. Kreuter;Kristen Olson;J. Wagner;Ting Yan;T. Ezzati;Carolina Casas;Michael Lemay;Andy Peytchev;R. Groves;T. Raghunathan
概括。 无答复加权是一种常用的方法，用于调整调查中单位无答复造成的偏差。理论和模拟表明，为了在不增加方差的情况下有效减少偏差，用于无响应权重调整的协变量需要与响应指标和调查结果变量高度相关。在实践中，这些要求提出了一个经常被忽视的挑战，因为这些协变量通常未被观察到或可能不存在。调查最近开始收集补充数据，例如采访者的观察结果和关键调查结果变量的其他代理衡量标准。就这些辅助变量与实际结果高度相关而言，这些变量是无响应调整的有希望的候选变量。在本研究中，我们研究了全国家庭成长调查、医疗支出小组调查、美国全国选举调查、欧洲社会调查和密歇根大学交通研究所调查的传统协变量和新辅助变量。我们提供代理措施与调查请求响应以及实际调查结果变量之间关联的实证估计。我们还比较了各种无响应模型下的未加权和加权估计。我们对多个组织针对多个主题的多个招聘方案进行的多项调查的结果表明，寻找合适的协变量进行无应答调整很困难，并且需要提高辅助数据的质量。
Summary. Non‐response weighting is a commonly used method to adjust for bias due to unit non‐response in surveys. Theory and simulations show that, to reduce bias effectively without increasing variance, a covariate that is used for non‐response weighting adjustment needs to be highly associated with both the response indicator and the survey outcome variable. In practice, these requirements pose a challenge that is often overlooked, because those covariates are often not observed or may not exist. Surveys have recently begun to collect supplementary data, such as interviewer observations and other proxy measures of key survey outcome variables. To the extent that these auxiliary variables are highly correlated with the actual outcomes, these variables are promising candidates for non‐response adjustment. In the present study, we examine traditional covariates and new auxiliary variables for the National Survey of Family Growth, the Medical Expenditure Panel Survey, the American National Election Survey, the European Social Surveys and the University of Michigan Transportation Research Institute survey. We provide empirical estimates of the association between proxy measures and response to the survey request as well as the actual survey outcome variables. We also compare unweighted and weighted estimates under various non‐response models. Our results from multiple surveys with multiple recruitment protocols from multiple organizations on multiple topics show the difficulty of finding suitable covariates for non‐response adjustment and the need to improve the quality of auxiliary data.