Randomized Recruitment Driven Sampling

2026-02-27

AI Summary

This study addresses the challenge of conducting representative sampling in contexts where conventional sampling frames are unavailable—such as during pandemics, natural disasters, or conflicts—and existing frameless methods like Respondent-Driven Sampling (RDS) suffer from substantial bias due to reliance on non-random recruitment. To overcome this limitation, the authors propose a novel approach, Randomized Respondent-Driven Sampling (RRDS), which integrates researcher-initiated random recruitment into the RDS framework. By leveraging mobile communication technologies and an enhanced chain-referral design, RRDS algorithmically enforces recruitment randomness and enables remote implementation. Simulations and a field experiment among garment workers in Bangladesh demonstrate that RRDS significantly reduces estimation bias and improves confidence interval coverage compared to traditional RDS, effectively approximating the ideal of random sampling and overcoming the bias bottleneck inherent in highly homophilous networks.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Application Domains: Humanities & Computational Social Science

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Respondent Driven Sampling
• sampling frame
• homophily
• survey bias
• hard-to-reach populations

Innovation

• Randomized Recruitment Driven Sampling
• Respondent Driven Sampling
• sampling bias
• homophily
• remote survey