Neighbourhood Bootstrap for Respondent-Driven Sampling

TL;DR

This paper introduces a new neighbourhood bootstrap method for estimating uncertainty in Respondent-Driven Sampling, addressing limitations of existing bootstrap techniques by providing more accurate variance estimates.

Contribution

The paper proposes a novel neighbourhood bootstrap approach for RDS, with theoretical consistency proof and empirical evaluation showing improved uncertainty quantification.

Findings

Neighbourhood bootstrap outperforms existing methods in simulations

The method provides more accurate variance estimates for RDS

Theoretical proof of consistency under certain conditions

Abstract

Respondent-Driven Sampling (RDS) is a form of link-tracing sampling, a sampling technique used 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. We prove the consistency of our method under some conditions and investigate its finite sample performance, through a simulation study, under realistic RDS sampling assumptions.