Neighborhood Bootstrap for Respondent-Driven Sampling. (26th February 2022)

• Record Type: Journal Article
• Authors: Yauck, Mamadou; Moodie, Erica E M; Apelian, Herak; Fourmigue, Alain; Grace, Daniel; Hart, Trevor A; Lambert, Gilles; Cox, Joseph
• Is Part Of: Journal of Survey Statistics and Methodology. Volume 10:Number 2(2022)
• Page Start: 419
• Page End: 438
• Publication Date: 2022-02-26
• Subjects: Hidden population sampling -- Resampling -- Respondent-driven sampling -- Simulations -- Social networks; Surveys -- Methodology -- Periodicals; Surveys -- Evaluation -- Periodicals; Sampling (Statistics) -- Periodicals; 001.433
• DOI: 10.1093/jssam/smab057
• Languages: English
• ISSNs: 2325-0984

Abstract

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. 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. proposed the 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 article, we propose the neighborhood bootstrap method for quantifying 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.