Randomized Recruitment Driven Sampling: A Cellphone Based Approach

• In Event: New Advances in Social Network Analysis

Abstract

Researchers are often interested in collecting representative surveys during times when a sampling a frame is expensive or not feasible, such as during pandemics, natural disasters, or armed conflict. Respondent Driven Sampling (RDS) does not require a sampling frame, yet non-random peer recruitment often introduces substantial bias, particularly under high homophily. We introduce Randomized Recruitment Driven Sampling (RRDS), a cellphone-based adaptation that incorporates researcher-controlled randomization into each recruitment wave. While standard RDS is necessary for stigmatized groups where network transparency is infeasible, RRDS is designed for low-stigma populations that become difficult to access due to logistical barriers. In these contexts, RRDS enforces the random recruitment assumption that traditional RDS relies upon but rarely achieves. Through simulation and an experiment surveying Bangladeshi garment workers during the COVID-19 pandemic, we demonstrate that RRDS produces less biased estimates and improved confidence interval coverage compared to traditional RDS. RRDS offers a scalable, remote-compatible alternative for studying low-stigma groups in challenging contexts where large-scale probability sampling is unsafe or infeasible.

Authors

• Adam Visokay, University of Washington

• Tyler McCormick, University of Washington

• Rachel Heath, University of Washington

• Laura Boudreau