Statistical and sociological development of respondent driven sampling

描述（由申请人提供）：根据定义，隐藏人群难以接触，但通常在科学研究中具有很大的兴趣;这些人群的例子包括无家可归者，注射毒品使用者和与男性发生性关系的男性。受访者驱动抽样（RDS）涉及同行招募他们的同行（通常是朋友和熟人）通过使用少量的编号优惠券每个参与者。RDS采用了“双重激励”制度，个人既参加了研究，又成功招募了其他合格的参与者。RDS不仅允许以这种方式有效地招募隐藏群体的成员，它还提供了关于不同亚群体之间的混合（也称为“同性恋”）和个人网络规模的信息，这两个重要因素构成了社交网络的结构。虽然RDS目前在世界各地的大量研究中使用，但需要开发和验证分析这些研究产生的数据的方法。我们的应用程序有三个首要目标：开发统计模型来分析同质性和网络规模，这是社交网络结构的两个重要特征;开发加权方案，纠正RDS样本的偏倚性质，允许应用标准的统计方法;并开发了一个模拟环境来研究我们的方法的统计特性和模型参数估计的鲁棒性假设。我们的应用程序融合了最先进的统计推断和计算建模，以解决社会学中长期存在的问题。应答者驱动抽样（RDS）是一种从“隐藏”人群中抽取个体的技术，例如无家可归者，注射毒品使用者和与男性发生性关系的男性，其中涉及同龄人招募同龄人。RDS不仅允许以这种方式有效地招募隐藏群体的成员，它还提供了关于不同亚群体之间的混合（也称为“同性恋”）和个人网络规模的信息，这两个重要因素构成了社交网络的结构。我们的应用程序有三个首要目标：开发统计模型来分析同质性和网络规模，这是社交网络结构的两个重要特征;开发加权方案，纠正RDS样本的偏倚性质，允许应用标准的统计方法;并开发了一个模拟环境来研究我们的方法的统计特性和模型参数估计的鲁棒性假设。

DESCRIPTION (provided by applicant): Hidden populations are, by definition, hard-to-reach, but are often of great interest in scientific studies; examples of such populations include the homeless, injection drug users, and men who have sex with men. Respondent- driven sampling (RDS) involves peers recruiting their peers (usually friends and acquaintances) through the use of a small number of uniquely-numbered coupons per participant. RDS employs a 'double incentive' system, where individuals are compensated both for participating in the study and for successfully recruiting other eligible participants. Not only does RDS allow members of hidden populations to be recruited efficiently in this manner, it also provides information on mixing between different subpopulations (otherwise known as 'homophily') and on network sizes of individuals, both important factors underlying the structure of social networks. Although RDS is currently being used in a large number of studies around the world, methodology to analyze the data being generated by these studies needs to be developed and validated. Our application has three overarching aims; to develop statistical models to analyze homophily and network size, two important features of the structure of social networks; to develop weighting schemes which correct for the biased nature of RDS samples, to allow standard statistical approaches to be applied; and to develop a simulation environment to investigate the statistical properties of our approaches and the robustness of parameter estimates to model assumptions. Our application melds state-of-the-art statistical inference and computational modeling in order to address long-standing questions in sociology. Respondent-driven sampling (RDS) is a technique to sample individuals from 'hidden' populations, such as the homeless, injection drug users, and men who have sex with men, which involves peers recruiting their peers. Not only does RDS allow members of hidden populations to be recruited efficiently in this manner, it also provides information on mixing between different subpopulations (otherwise known as 'homophily') and on network sizes of individuals, both important factors underlying the structure of social networks. Our application has three overarching aims; to develop statistical models to analyze homophily and network size, two important features of the structure of social networks; to develop weighting schemes which correct for the biased nature of RDS samples, to allow standard statistical approaches to be applied; and to develop a simulation environment to investigate the statistical properties of our approaches and the robustness of parameter estimates to model assumptions.