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: 8469255
• 负责人: Elena Erosheva
• 金额: $ 17.97万
• 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 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8469255
• 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集，称为响应者-7驱动抽样(RDS)，是许多研究难以接触到或隐藏的人群的研究人员目前选择的方法。RDS利用社交网络，从一小部分人开始，允许每一波受访者从他们的联系人中招募下一波样本。然而，RDS估计者关于特定人群的网络结构和连锁推荐招聘过程的重要假设是否得到满足，往往不清楚。[在这个项目中，我们将(1)从Egocenti数据中推断出对RDS可行性很重要的关系结构；(2)开发一个 评估RDS可行性的综合模拟研究框架；以及(3)将模型辅助的方法扩展到从RDS数据进行推理，以考虑人口聚集。我们将把这些新方法应用于GLBT老年人的社会网络规模和结构的独特观测数据，这些数据来自骄傲的养育和老龄化，以便为社会网络和RDS链推荐过程的计算机模拟提供信息，以便系统地研究这个难以到达的人群中潜在的RDS估计者的质量。我们将在R-Package ASAnalyst中提供这些方法，以便应用RDS的研究人员可以在申请传统调查研究的资金之前，以类似于样本量计算的方式来决定是否保证RDS。]