Estimating uncertainty in respondent-driven sampling using a tree bootstrap method.

ABSTRACT: Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also c

SUBMITTER: Baraff AJ 

PROVIDER: S-EPMC5187726 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature