Statistical and sociological development of respondent driven sampling

描述(由申请人提供)：隐蔽人群的定义是很难接触到的，但往往对科学研究非常感兴趣；这类人群的例子包括无家可归者、注射吸毒者和男男性行为者。受访者驱动抽样(RDS)是指参与者通过使用少量唯一编号的优惠券来招募他们的同龄人(通常是朋友和熟人)。RDS采用了“双重激励”制度，个人因参与研究和成功招募其他符合条件的参与者而获得补偿。RDS不仅允许以这种方式有效地招募隐藏群体的成员，它还提供关于不同亚群体之间的混合(也称为“同源”)和个人网络规模的信息，这两个因素都是构成社会网络结构的重要因素。尽管RDS目前正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样本的偏差性质，允许应用标准统计方法；以及开发模拟环境，以调查我们方法的统计特性和参数估计对模型假设的稳健性。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。受访者驱动抽样(RDS)是一种从“隐藏”人群中抽样的技术，例如无家可归者、注射吸毒者和男男性行为者，这涉及到同伴招募他们的同伴。RDS不仅允许以这种方式有效地招募隐藏群体的成员，它还提供关于不同亚群体之间的混合(也称为“同源”)和个人网络规模的信息，这两个因素都是构成社会网络结构的重要因素。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样本的偏差性质，允许应用标准统计方法；以及开发模拟环境，以调查我们方法的统计特性和参数估计对模型假设的稳健性。

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.