Empirical Assessment of Respondent Driven Sampling from Total Survey Error Perspectives

本研究项目将对受访者驱动抽样（RDS）数据收集的现实情况进行实证调查。RDS是一种对稀有或隐藏种群进行抽样的新方法。该方法从目标人群的成员开始，追踪他们的社会网络以及与他们有联系的人的网络。尽管这种方法越来越受欢迎，但公开可用的RDS数据集还很稀缺。因此，对RDS的方法学评估非常有限。本研究项目将对RDS进行理论驱动的方法学评估。该项目将为开发RDS研究的设计功能提供一个平台，以最大限度地减少潜在的错误和对关键假设的违反。通过制定适当的推理策略，可以在RDS方法收集的数据中更充分地代表稀有或隐藏的种群。如果没有这些类型的检查，通过RDS收集的行为和社会科学数据就有可能以未知的方式错误地描述罕见或隐藏的人群。在某种程度上，这些数据为公共政策提供了信息，稀有或隐藏的人群可能会从这项研究中受益。将生成新的数据集并向公众提供，以促进本项目以外的方法学研究。本研究项目将在总调查误差（Total Survey Error， TSE）框架下对RDS进行实证评估。TSE是一种调查方法框架，允许对错误进行系统检查。该项目旨在通过检查抽样生产率、误差特性和可复制性来改进当前的RDS数据收集和推断实践。调查人员将收集洛杉矶县两个罕见种群的数据，这些种群的外部概率样本数据是可用的。以概率为基础的数据将被用作“金标准”，与收集到的RDS数据的估计值进行比较，提供一个独特的机会来评估RDS不仅是一种抽样方法，而且是一种数据收集方法。数据集将根据经验为模拟研究提供信息，该研究将检查各种网络结构和响应倾向对抽样生产率和推断误差的影响。该项目由方法、测量和统计项目和联邦统计机构联盟支持，作为支持调查和统计方法研究的联合活动的一部分。

This research project will provide an empirical investigation into the realities of respondent driven sampling (RDS) data collection. RDS is a new method for sampling rare or hidden populations. The method starts with the members of the target population and traces their social networks as well as the networks of those to whom they are connected. Although this method is growing in popularity, there is a scarcity of publicly available RDS datasets. As a result, methodological assessments of RDS are very limited. This research project will conduct a theory-drive methodological assessment of RDS. The project will provide a platform to develop design features for RDS studies that minimize potential errors and violations of critical assumptions. By developing appropriate inference strategies, rare or hidden populations will be more fully represented in the data collected by RDS methods. Absent these types of examinations, behavioral and social science data collected through RDS runs the risk of mischaracterizing rare or hidden populations in unknown ways. To the extent that these data inform public policies, rare or hidden populations may benefit from this research. New datasets will be generated and made publicly available to promote methodological research beyond this project.This research project will conduct an empirical assessment RDS within the Total Survey Error (TSE) framework. TSE is a framework in survey methodology that allows for the systematic examination of errors. This project seeks to improve current RDS data collection and inference practices by examining sampling productivity, error properties, and replicability. The investigators will collect data on two rare populations in Los Angeles County for which external probability-based sample data are available. The probably-based data will be used as "gold standards" against which estimates from the RDS collected data will be compared, providing a unique opportunity to assess RDS not only as a sampling method but also as a data collection method. The data sets will empirically inform a simulation study that will examine the effects of various network structures and response propensities on sampling productivity and inferential errors. The project is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.