Bias-Variance and Breadth-Depth Tradeoffs in Respondent-Driven Sampling

TL;DR

This paper evaluates the bias-variance tradeoffs in respondent-driven sampling (RDS) estimators, comparing the standard Volz-Heckathorn estimator with the plain mean across different network types and referral patterns through simulations.

Contribution

It provides a comprehensive simulation-based analysis of RDS estimators' performance under various network structures and recruitment behaviors, highlighting conditions where the plain mean outperforms VH.

Findings

Plain mean often outperforms VH estimator in homophily networks.

Multiple recruitment and referral patterns significantly affect RDS depth.

Simulation results inform better estimator choice in practice.

Abstract

Respondent-driven sampling (RDS) is a link-tracing network sampling strategy for collecting data from hard-to-reach populations, such as injection drug users or individuals at high risk of being infected with HIV. The mechanism is to find initial participants (seeds), and give each of them a fixed number of coupons allowing them to recruit people they know from the population of interest, with a mutual financial incentive. The new participants are given coupons again and the process repeats. Currently, the standard RDS estimator used in practice is known as the Volz-Heckathorn (VH) estimator. It relies on strong assumptions about the underlying social network and the RDS process. Via simulation, we study the relative performance of the plain mean and VH estimator when assumptions of the latter are not satisfied, under different network types (including homophily and rich-get-richer networks), participant referral patterns, and varying number of coupons. The analysis demonstrates that the plain mean outperforms the VH estimator in many but not all of the simulated settings, including homophily networks. Also, we highlight the implications of multiple recruitment and varying referral patterns on the depth of RDS process. We develop interactive visualizations of the findings and RDS process to further build insight into the various factors contributing to the performance of current RDS estimation techniques.