Doctoral Dissertation Research: Extending Respondent-Driven Sampling to Social Norm Research and Development of Web-Based RDS Software

博士论文研究：将受访者驱动的抽样扩展到社会规范研究和基于 Web 的 RDS 软件开发

• 批准号: 0718377
• 负责人: Douglas Heckathorn
• 金额: $ 1.2万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0718377&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Extending; Respondent

This project will make several contributions to Respondent-Driven Sampling methodology (RDS), a new statistical method now widely used in the study of hidden and hard to reach populations, through analysis of web-based RDS (WebRDS) samples of undergraduate students from two U.S. universities of varying selectivity. First, the project extends RDS to the study of social norms within the context of social network structure by mapping enforcement, compliance, and perception of knowledge-seeking norms onto the underlying social network structure of each population. RDS is especially well suited for such analysis because it provides a random sample of behaviorally defined ties from within a social network. The overall social network structure will be analyzed based on these ties and combined with survey questions regarding the social norms of respondents who make up those ties using the RDS homophily and affiliation indices. Consequently, the research will provide important insights into the interaction between social norms and social networks. Second, the project develops and tests WebRDS software, a fully automated online variant of RDS, for distribution to the general scientific community. Finally, while the RDS estimator has been shown to be asymptotically unbiased analytically and computationally, the hidden nature of most RDS study populations has prevented large scale, multi-site empirical validation. This project will provide such validation through comparison of RDS estimates with institutional data from two large undergraduate populations.The larger research community will benefit from the methods developed in this project. First, the development of WebRDS software will provide for very fast and efficient sampling of electronically connected institutions. This will be especially useful for case control studies of epidemiological outbreaks where a reduction in time spent on data collection can prevent additional infections. The method of social norm research developed in this project can be applied to any outcome variable that is potentially spread through interpersonal contact. More immediately, the project will study knowledge-seeking norms in university students, a population recently identified by the U.S. Department of Education as declining in quality. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career. The research 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.

该项目将通过分析来自美国两所大学的基于网络的RDS（WebRDS）本科生样本，对应答者驱动抽样方法（RDS）做出一些贡献。RDS是一种新的统计方法，目前广泛用于研究隐藏和难以接触的人群。 首先，该项目扩展RDS的社会规范的研究范围内的社会网络结构的映射执法，遵守和感知的知识寻求规范到每个人口的基本社会网络结构。 RDS特别适合此类分析，因为它提供了社交网络内行为定义关系的随机样本。 整体的社会网络结构将分析这些关系的基础上，并结合调查问题的社会规范的受访者谁弥补这些关系使用RDS的同质性和从属关系指数。 因此，该研究将为社会规范和社交网络之间的相互作用提供重要的见解。 第二，该项目开发和测试WebRDS软件，这是RDS的一个完全自动化的在线变体，用于分发给一般科学界。 最后，虽然RDS估计已被证明是渐近无偏的分析和计算，大多数RDS研究人群的隐藏性质，阻止了大规模，多站点的实证验证。 该项目将通过比较RDS估计值与来自两个大型大学生群体的机构数据来提供这种验证。更大的研究社区将受益于该项目开发的方法。 首先，WebRDS软件的开发将提供对电子连接机构的非常快速和有效的抽样。 这对于流行病暴发的病例控制研究特别有用，因为减少数据收集时间可以防止更多的感染。 本研究所开发的社会规范研究方法可应用于任何可能通过人际接触传播的结果变量。 更直接的是，该项目将研究大学生寻求知识的规范，美国教育部最近确定这一群体的质量正在下降。 作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立的研究生涯。 这项研究得到了方法、测量和统计方案以及联邦统计机构联合会的支持，作为支持调查和统计方法研究的联合活动的一部分。