The effect of differential recruitment, non-response and non-recruitment on estimators for respondent-driven sampling

Abstract

Respondent-driven sampling is a widely-used network sampling technique, designed to sample from hard-to-reach populations. Estimation from the resulting samples is an area of active research, with software available to compute at least four estimators of a population proportion. Each estimator is claimed to address deficiencies in previous estimators, however those claims are often unsubstantiated. In this study we provide a simulation-based comparison of five existing estimators, focusing on sampling conditions which a recent estimator is designed to address. We find no estimator consistently out-performs all others, and highlight sampling conditions in which each is to be preferred.