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Activity Number: 285 - New Advances in Sample Design and Adjusting for Survey Nonresponse
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
Sponsor: Survey Research Methods Section
Abstract #319144
Title: A Bayesian Approach to Differential Recruitment with Respondent-Driven Sampling Data
Author(s): Isabelle Beaudry*
Companies: Pontificia Universidad Católica de Chile
Keywords: Respondent-Driven sampling; Survey Sampling methodology; Hard-to-reach populations; Model-Based inference; Non-response bias; Network inference

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 wave is 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 contribution of this work is to parameterize an alternative recruitment behavior and propose a Bayesian estimator to correct for nonrandom recruitment.

Authors who are presenting talks have a * after their name.

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