受访者驱动抽样：对当前方法的评估
Respondent-Driven Sampling: An Assessment of Current Methodology
摘要：
响应者驱动的抽样（RDS）采用一种链接跟踪网络抽样策略的变体，从难以接触的人群中收集数据。通过跟踪底层社会网络中的链接，该过程利用社会结构来扩展样本并减少其对初始（便利）样本的依赖。RDS的主要目标通常是估计难以接触人口的平均数。目前的估计数作出了强有力的假设，以便将数据作为概率样本处理。特别是，我们评估了三个关键的敏感性估计：初始样本引起的偏差，受访者行为的不可控特征，以及抽样的无替换结构。这份文件对铁路数据系统的使用者是一个警告。虽然目前的RDS方法是强大的和聪明的，有利的统计特性声称目前的估计被证明是严重依赖于往往不切实际的假设。
英文摘要：
Respondent-Driven Sampling (RDS) employs a variant of a link-tracing network sampling strategy to collect data from hard-to-reach populations. By tracing the links in the underlying social network, the process exploits the social structure to expand the sample and reduce its dependence on the initial (convenience) sample. The primary goal of RDS is typically to estimate population averages in the hard-to-reach population. The current estimates make strong assumptions in order to treat the data as a probability sample. In particular, we evaluate three critical sensitivities of the estimators: to bias induced by the initial sample, to uncontrollable features of respondent behavior, and to the without-replacement structure of sampling. This paper sounds a cautionary note for the users of RDS. While current RDS methodology is powerful and clever, the favorable statistical properties claimed for the current estimates are shown to be heavily dependent on often unrealistic assumptions.
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