Randomized Recruitment Driven Sampling

TL;DR

This paper introduces RRDS, a cellphone-based sampling method that enforces random recruitment to reduce bias in surveys of low-stigma populations where traditional RDS is unreliable.

Contribution

The paper proposes and evaluates RRDS, a novel adaptation of RDS that incorporates researcher-controlled randomization to improve sampling accuracy in challenging contexts.

Findings

RRDS produces less biased estimates than traditional RDS.

RRDS improves confidence interval coverage.

RRDS is scalable and suitable for remote surveys in difficult environments.

Abstract

Surveys are critical inputs for research and policy, yet, enumerating a sampling frame is logistically infeasible or financially nonviable in many circumstances, 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 and evaluate Randomized Recruitment Driven Sampling (RRDS), a cellphone-based adaptation of RDS 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.