Network Sampling: From Snowball and Multiplicity to Respondent-Driven Sampling
Network Sampling: From Snowball and Multiplicity to Respondent-Driven Sampling
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DOI：
10.1146/annurev-soc-060116-053556
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发表时间：
2017-01-01
期刊：
影响因子：
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通讯作者：
Cameron, Christopher J.
中科院分区：
文献类型：
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作者：
Heckathorn, Douglas D.;Cameron, Christopher J.
网络抽样作为一套方法出现，用于从难以接触的人口中抽取统计上有效的样本。网络抽样的第一种形式是多重抽样，询问受访者对其个人网络中的人产生影响的事件;随后将其应用于凶杀、艾滋病毒和其他主题的研究，但其用途仅限于公共事件。链接追踪设计采用了一种不同的方法来研究难以接触的人群，使用一组受访者，随着每一轮受访者招募他们的同龄人，这些受访者会一波一波地扩大。应用于隐藏种群的链接追踪，通常被描述为滚雪球抽样，最初被认为是一种便利抽样形式。这种情况随着响应者驱动抽样（RDS）的发展而改变，RDS是一种广泛使用的网络抽样方法，其中链接跟踪设计适用于为统计推断提供基础。关于RDS的文献数量庞大且迅速扩大，涉及许多独立研究小组的贡献，这些研究小组采用了来自数十个不同国家的数据。在这些文献中，许多重要的研究问题仍然没有解决，包括如何最好地选择替代RDS估计，如何完善现有的估计，使他们更少地依赖于假设，有时是反事实的，也许是最大的未解决的问题，如何最好地计算估计的变异性。
Network sampling emerged as a set of methods for drawing statistically valid samples of hard-to-reach populations. The first form of network sampling, multiplicity sampling, involved asking respondents about events affecting those in their personal networks; it was subsequently applied to studies of homicide, HIV, and other topics, but its usefulness is limited to public events. Link-tracing designs employ a different approach to study hard-to-reach populations, using a set of respondents that expands in waves as each round of respondents recruit their peers. Link-tracing as applied to hidden populations, often described as snowball sampling, was initially considered a form of convenience sampling. This changed with the development of respondent-driven sampling (RDS), a widely used network sampling method in which the link-tracing design is adapted to provide the basis for statistical inference. The literature on RDS is large and rapidly expanding, involving contributions by numerous independent research groups employing data from dozens of different countries. Within this literature, many important research questions remain unresolved, including how best to choose among alternative RDS estimators, how to refine existing estimators to make them less dependent on assumptions that are sometimes counterfactual, and perhaps the greatest unresolved issue, how best to calculate the variability of the estimates.