Respondent-Driven Sampling is type of link-tracing network sampling used to study hard-to-reach populations. Beginning with a convenience sample, each person sampled is given 2-3 uniquely identified coupons to distribute to other members of the target population, making them eligible for enrollment in the study. This is effective at collecting large diverse samples from many populations.
Due to the complexity of the sampling process, inference for the most fundamental of population features: population proportion, is challenging, and has been the subject of much work in recent years, typically using only data on local network size and the variable of interest.
This talk focuses on work that considers inferential goals addressed using multiple variables measured on participants. We describe using data on local network composition for a variable biasing recruitment to adjust for preferential recruitment, semi-parametric testing for bivariate associations in the RDS dataset, and methods for clustering RDS participants based on covariate and referral data.