Empirical Assessment of Respondent Driven Sampling from Total Survey Error Perspectives

从总调查误差角度对受访者驱动抽样进行实证评估

• 批准号: 1461470
• 负责人: Sunghee Lee
• 金额: $ 55万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1461470&HistoricalAwards=false
• 关键词: Empirical; Assessment; Respondent; Driven; Sampling

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.

这项研究项目将对受访者驱动抽样(RDS)数据收集的现实进行实证调查。RDS是一种抽样稀有或隐藏种群的新方法。该方法从目标人群的成员开始，跟踪他们的社交网络以及与他们有联系的人的网络。虽然这种方法越来越受欢迎，但公开可用的RDS数据集仍然很少。因此，对RDS的方法论评估非常有限。本研究项目将对RDS进行理论驱动的方法论评估。该项目将提供一个平台，为RDS研究开发设计特点，将潜在的错误和违反关键假设的情况降至最低。通过制定适当的推理策略，稀有或隐藏的种群将在RDS方法收集的数据中得到更充分的表现。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可能会以未知的方式误判稀有或隐藏的人群。就这些数据为公共政策提供信息的程度而言，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公布，以促进该项目以外的方法学研究。该研究项目将在总调查误差(TSE)框架内进行经验评估RDS。TSE是调查方法中的一个框架，允许系统地检查错误。该项目旨在通过检查抽样生产率、错误属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人口的数据，这些人口的外部概率样本数据是可用的。以可能为基础的数据将被用作“黄金标准”，与从“铁路发展策略”收集的数据所作的估计进行比较，从而提供一个独特的机会，以评估“铁路发展策略”不仅作为一种抽样方法，而且作为一种数据收集方法。这些数据集将为一项模拟研究提供经验性的信息，该模拟研究将检查各种网络结构和反应倾向对采样生产率和推断错误的影响。作为支持调查和统计方法研究的联合活动的一部分，该项目得到了方法学、测量和统计方案和一个联邦统计机构联盟的支持。