In a probability sampling survey, adaptive data collection strategies may be used to obtain a response set that minimizes nonresponse bias within budget constraints. Previous research has stratified the target population into subgroups defined by categories of auxiliary variables observed for the entire population, and tailored strategies to obtain similar response rates across subgroups. However, if the auxiliary variables are weakly correlated with the target survey variables, optimizing data collection for these subgroups may not reduce nonresponse bias and may actually increase the variance of survey estimates. In this paper, we propose a stratification method to identify subgroups by: (1) predicting values of target survey variables from auxiliary variables, and (2) forming subgroups with different response propensities based on the predicted values of target survey variables. By tailoring different data collection strategies to these subgroups, we can obtain a response set with less variation in response propensities across subgroups that are directly relevant to the target survey variables. Given this rationale, we also propose to measure nonresponse bias by the coefficient of variation of response propensities estimated from the predicted target survey variables. A case study using the Dutch Health Survey shows that the proposed stratification method generally produces less variation in response propensities with respect to the predicted target survey variables compared to traditional methods, thereby leading to a response set that better resembles the population.

中文翻译：

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一种在贝叶斯自适应调查设计中解决无响应偏差的最佳分层方法

在概率抽样调查中，可以使用自适应数据收集策略来获得一个响应集，该响应集在预算限制内最大限度地减少非响应偏差。以前的研究将目标人群分层为由为整个人群观察到的辅助变量类别定义的亚组，并定制策略以获得跨亚组的相似响应率。但是，如果辅助变量与目标调查变量的相关性较弱，则优化这些子组的数据收集可能不会减少无响应偏差，实际上可能会增加调查估计值的方差。在本文中，我们提出了一种分层方法来识别子组：（1） 从辅助变量预测目标调查变量的值，以及 （2） 根据目标调查变量的预测值形成具有不同响应倾向的子组。通过为这些子组定制不同的数据收集策略，我们可以获得一个响应集，该响应集在与目标调查变量直接相关的子组之间的响应倾向变化较小。鉴于这一基本原理，我们还建议通过从预测目标调查变量估计的响应倾向的变异系数来衡量无响应偏差。使用荷兰健康调查的案例研究表明，与传统方法相比，所提出的分层方法通常对预测目标调查变量产生的响应倾向变化较小，从而产生更接近总体的响应集。