Thompson S K
Department of Statistics, Pennsylvania State University, University Park 16802, USA.
NIDA Res Monogr. 1997;167:296-319.
Studies of populations such as drug users encounter difficulties because the members of the populations are rare, hidden, or hard to reach. Conventionally designed large-scale surveys detect relatively few members of the populations so that estimates of population characteristics have high uncertainty. Ethnographic studies, on the other hand, reach suitable numbers of individuals only through the use of link-tracing, chain referral, or snowball sampling procedures that often leave the investigators unable to make inferences from their sample to the hidden population as a whole. In adaptive sampling, the procedure for selecting people or other units to be in the sample depends on variables of interest observed during the survey, so the design adapts to the population as encountered. For example, when self-reported drug use is found among members of the sample, sampling effort may be increased in nearby areas. Types of adaptive sampling designs include ordinary sequential sampling, adaptive allocation in stratified sampling, adaptive cluster sampling, and optimal model-based designs. Graph sampling refers to situations with nodes (for example, people) connected by edges (such as social links or geographic proximity). An initial sample of nodes or edges is selected and edges are subsequently followed to bring other nodes into the sample. Graph sampling designs include network sampling, snowball sampling, link-tracing, chain referral, and adaptive cluster sampling. A graph sampling design is adaptive if the decision to include linked nodes depends on variables of interest observed on nodes already in the sample. Adjustment methods for nonsampling errors such as imperfect detection of drug users in the sample apply to adaptive as well as conventional designs.
对吸毒者等人群的研究面临困难，因为这些人群的成员稀少、隐匿或难以接触到。传统设计的大规模调查发现的该人群成员相对较少，因此对人群特征的估计具有很高的不确定性。另一方面，人种学研究只有通过使用联系追踪、链式推荐或滚雪球抽样程序才能接触到足够数量的个体，但这些程序往往使研究人员无法从样本推断整个隐匿人群的情况。在适应性抽样中，选择纳入样本的人或其他单位的程序取决于调查过程中观察到的感兴趣变量，因此设计会根据实际遇到的人群进行调整。例如，当在样本成员中发现自我报告的吸毒情况时，可以在附近地区增加抽样力度。适应性抽样设计的类型包括普通序贯抽样、分层抽样中的适应性分配、适应性整群抽样和基于最优模型的设计。图抽样是指节点（例如人）由边（如社会联系或地理 proximity）连接的情况。首先选择节点或边的初始样本，随后沿着边将其他节点纳入样本。图抽样设计包括网络抽样、滚雪球抽样、联系追踪、链式推荐和适应性整群抽样。如果纳入相连节点的决定取决于在已纳入样本的节点上观察到的感兴趣变量，那么图抽样设计就是适应性的。针对样本中吸毒者检测不完美等非抽样误差的调整方法适用于适应性设计和传统设计。
