Respondent-Driven Sampling: An Assessment of Current Methodology.

Respondent-Driven Sampling: An Assessment of Current Methodology.
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DOI:
10.1111/j.1467-9531.2010.01223.x
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发表时间:
2010-08
影响因子:
3
通讯作者:
Handcock MS
Handcock MS
中科院分区:
法学2区
文献类型:
--
作者:
Gile KJ;Handcock MS

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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。目前人口平均数的估计数作出了强有力的假设,以便将数据视为概率样本。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。我们的分析表明:(1)种子的便利样本可能会导致偏差,RDS中通常使用的样本波的数量可能不足以获得公认的渐近无偏性所需的节点混合类型;(2)受访者的优先推荐行为导致偏差;(3)当对目标人群的相当大一部分进行采样时,当前估计量可能会有相当大的偏差。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。我们提出了改进方法的建议。
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 current estimators of population averages make strong assumptions in order to treat the data as a probability sample. 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. Our analysis indicates: (1) that the convenience sample of seeds can induce bias, and the number of sample waves typically used in RDS is likely insufficient for the type of nodal mixing required to obtain the reputed asymptotic unbiasedness; (2) that preferential referral behavior by respondents leads to bias; (3) that when a substantial fraction of the target population is sampled the current estimators can have substantial bias. 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. We recommend ways to improve the methodology.
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