Title: A Bayesian approach to differential recruitment with respondent-driven sampling data
Authors: Isabelle Beaudry - Pontificia Universidad Catolica de Chile (Chile) [presenting]
Krista Gile - University of Massachusetts Amherst (United States)
Abstract: Respondent-driven sampling (RDS) is a sampling mechanism that has proven very effective to sample hard-to-reach human populations connected through social networks. A small number of individuals typically known to the researcher are initially sampled and asked to recruit a small fixed number of their contacts who are also members of the target population. Each subsequent sampling waves are produced by peer recruitment until the desired sample size is achieved. However, the researcher's lack of control over the sampling process has posed several challenges to producing valid statistical inference from RDS data. For instance, participants are generally assumed to recruit completely at random among their contacts despite the growing empirical evidence that suggests otherwise and the substantial sensitivity of most RDS estimators to this assumption. The main contributions are to parameterize an alternative recruitment behavior and propose a Bayesian estimator to correct for nonrandom recruitment.