Respondent-driven sampling for highly structured populations

针对高度结构化人群的受访者驱动抽样

• 批准号: 8639437
• 负责人: Elena Erosheva
• 金额: $ 20.49万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8639437
• 关键词: Accounting; Adult; Affect; Age Distribution; Aging; Basic Science; Behavior; Bisexual; Caring; Centers for Disease Control and Prevention (U.S.); Complex; Computer Simulation; Computer software; Data; Data Collection; Development; Education; Elderly; Feasibility Studies; Funding; Future; Gays; Gender Identity; Health; Heterosexuals; Income; Individual; Intervention; Knowledge; Lesbian; Mental Depression; Methodology; Methods; Modeling; Network-based; Participant; Pilot Projects; Population; Prevalence; Prevention strategy; Procedures; Process; Property; Recruitment Activity; Research; Research Design; Research Personnel; Respondent; Sample Size; Sampling; Sampling Studies; Sex Orientation; Social Network; Societies; Specific qualifier value; Statistical Methods; Structure; Surveys; Target Populations; Techniques; Time; Work; base; disorder prevention; health disparity; improved; member; network models; simulation; social; social stigma; successful intervention; transgender

DESCRIPTION (provided by applicant): A network-based type of sampling technique and the corresponding set of estimates, known as Respondent-7 Driven Sampling (RDS), is the current method of choice for many researchers studying hard-to-reach or hidden populations. RDS exploits social networks by starting with a small set of individuals and allowing the respondents at each wave to recruit the next wave of the sample from their contacts. However, it is often unclear whether important assumptions of RDS estimators about the population-specific network structure and the chain-referral recruitment process are satisfied. [In this project, focusing on population clustering structures,we will (1) Infer relational structures from egocentri data that are important for RDS feasibility; (2) develop a comprehensive simulation study framework for assessing RDS feasibility; and (3) extend the model-assistedapproach to inference from RDS data to account for population clustering. We will apply these new methodsto unique observational data on the size and structure of social networks of older GLBT adults from the studyCaring and Aging with Pride to inform computer simulations of both social networks and RDS chain-referralprocesses in order to systematically study the quality of potential RDS estimators in this hard-to-reach population.We will make these methods available in the R-package ASAnalyst so they can be used by applied RDS researchers to decide whether RDS is warranted in a fashion similar to the sample size computation prior to a funding request for traditional survey research.]

描述（由申请人提供）：一种基于网络的抽样技术和相应的估计值集，被称为响应者-7驱动抽样（RDS），是许多研究难以接触或隐藏人群的研究人员当前选择的方法。RDS利用社交网络，从一小群人开始，让每一波的受访者从他们的联系人中招募下一波样本。然而，它往往是不清楚的RDS估计的人口特定的网络结构和链推荐招聘过程的重要假设是否得到满足。[In本项目的重点是人口聚集结构，我们将（1）从自我中心数据中推断关系结构，这对RDS的可行性很重要;（2）开发一个 综合模拟研究框架评估RDS的可行性;（3）扩展模型辅助的方法，从RDS数据的推断，以考虑人口聚集。我们将把这些新方法应用于研究中关于老年GLBT成年人社交网络的大小和结构的独特观察数据，以告知社交网络和RDS链的计算机模拟过程，以便系统地研究这个难以达到的人群中潜在RDS估计的质量。软件包ASAnalyst，以便应用RDS的研究人员可以使用它们来决定RDS是否以类似于传统调查研究的资金请求之前的样本量计算的方式得到保证。