Estimating uncertainty in respondent-driven sampling using a tree bootstrap method
Estimating uncertainty in respondent-driven sampling using a tree bootstrap method
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DOI：
10.1073/pnas.1617258113
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发表时间：
2016-12-20
影响因子：
11.1
通讯作者：
Raftery, Adrian E.
中科院分区：
文献类型：
--
作者：
Baraff, Aaron J.;McCormick, Tyler H.;Raftery, Adrian E.
受访者驱动抽样 (RDS) 是一种基于网络的链式推荐抽样形式，用于估计使用标准调查工具难以获取的人群属性。尽管自推出以来它已迅速普及，但 RDS 估计的统计特性仍然难以捉摸。特别是，这些估计值的抽样变异性已被证明比之前承认的要高得多，甚至设计用于解释 RDS 的方法也会导致误导性的狭窄置信区间。在本文中，我们介绍了一种树引导方法，用于基于重新采样招募树来估计 RDS 估计中的不确定性。我们使用已知社交网络的模拟来表明，树引导方法不仅优于现有方法，而且即使在具有高设计效果的极端情况下，也能捕获 RDS 的高可变性。我们还将该方法应用于乌克兰注射吸毒者的数据。与其他方法不同，树引导仅取决于采样的招聘树的结构，而不取决于对受访者测量的属性，因此可以估计属性之间的相关性以及变异性。我们的结果表明，准确评估 RDS 固有的高度不确定性是可能的。
Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects. We also apply the method to data from injecting drug users in Ukraine. Unlike other methods, the tree bootstrap depends only on the structure of the sampled recruitment trees, not on the attributes being measured on the respondents, so correlations between attributes can be estimated as well as variability. Our results suggest that it is possible to accurately assess the high level of uncertainty inherent in RDS.