Inference from Respondent-Driven sampling data

Seminars 2017

Speaker: Isabelle Beaudry, Pontificia Universidad Católica de Chile​

Place: Seminars room, Departamento de Matemática (Building F3, second floor), Valparaíso

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 a 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 objective of this talk is to describe this sampling method and traditional estimators to infer population prevalence as well as to discuss alternative methodologies to address known sources of bias, such as the bias arising from nonrandom recruitment.

JA slide show