Neighbourhood Bootstrap for Respondent-Driven Sampling

Respondent-Driven Sampling (RDS) is a form of link-tracing sampling, a sampling technique for `hard-to-reach' populations that aims to leverage individuals' social relationships to reach potential participants. While the methodological focus has been restricted to the estimation of population proportions, there is a growing interest in the estimation of uncertainty for RDS as recent findings suggest that most variance estimators underestimate variability. Recently, Baraff et al. (2016) proposed the \textit{tree bootstrap} method based on resampling the RDS recruitment tree, and empirically showed that this method outperforms current bootstrap methods. However, some findings suggest that the tree bootstrap (severely) overestimates uncertainty. In this paper, we propose the \textit{neighbourhood} bootstrap method for quantifiying uncertainty in RDS, and empirically show that our method outperforms the tree bootstrap in terms of bias and coverage under realistic RDS sampling assumptions.