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.
