Respondent-driven sampling: A new approach to the study of hidden populations
Respondent-driven sampling: A new approach to the study of hidden populations
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DOI：
10.1525/sp.1997.44.2.03x0221m
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发表时间：
1997-05-01
期刊：
影响因子：
3.2
通讯作者：
Heckathorn, DD
中科院分区：
文献类型：
--
作者：
Heckathorn, DD
当不存在抽样框架，并且公开承认该群体的成员资格具有潜在威胁时，该群体是“隐藏的”。进入这样的人群是困难的，因为标准的概率抽样方法产生的回复率和反应缺乏坦诚。对这些人群进行抽样的现有程序，包括滚雪球和其他连锁转诊样本、重要信息提供者方法和有针对性的抽样，在其样本中引入了充分证明的偏见。本文介绍了链式推荐抽样的一种新变体，即被访者驱动抽样，它采用结构化激励的双重系统来克服此类抽样的一些缺陷。利用马尔可夫链理论和有偏差网络理论的理论分析表明，该方法可以减少与链式推荐方法相关的偏差。分析包括一个证明，即使抽样从一组任意选择的初始对象开始，就像大多数链参考样本一样，最终样本的组成完全独立于这些初始对象。分析还包括理论规范的条件下，该程序产生无偏的样本。基于对康涅狄格州277名活跃毒品注射者的调查得出的实证结果支持了这些结论。最后，结论部分讨论了受调查者驱动的抽样如何改善网络抽样和民族志调查。
A population is ''hidden'' when no sampling frame exists and public acknowledgment of membership in the population is potentially threatening. Accessing such populations is difficult because standard probability sampling methods produce law response rates and responses that lack candor. Existing procedures for sampling these populations, including snowball and other chain-referral samples, the hey-informant approach, and targeted sampling, introduce well-documented biases into their samples. This paper introduces a new variant of chain-referral sampling, respondent-driven sampling, that employs a dual system of structured incentives to overcome some of the deficiencies of such samples. A theoretic analysis, drawing on both Markov-chain theory and the theory of biased networks, shows that this procedure can reduce the biases generally associated with chain-referral methods. The analysis includes a proof showing that even though sampling begins with an arbitrarily chosen set of initial subjects, as do most chain-referral samples, the composition of the ultimate sample is wholly independent of those initial subjects. The analysis also includes a theoretic specification of the conditions under which the procedure yields unbiased samples. Empirical results, based on surveys of 277 active drug injectors in Connecticut, support these conclusions. Finally, the conclusion discusses how respondent-driven sampling can improve both network sampling and ethnographic 44investigation.