Novel Sampling Design for Respondent-driven Sampling

TL;DR

This paper introduces anti-cluster RDS (AC-RDS), a modified sampling method that improves the mixing and reduces bias in respondent-driven sampling by addressing community bottlenecks in social networks.

Contribution

The paper proposes AC-RDS, an adjustment to standard RDS, and demonstrates its improved spectral gap, faster mixing, and reduced variance through theoretical analysis and empirical validation.

Findings

AC-RDS has a larger spectral gap than standard RDS.

AC-RDS reduces sample covariance and variance.

Empirical tests confirm improved sampling efficiency.

Abstract

Respondent-driven sampling (RDS) is a method of chain referral sampling popular for sampling hidden and/or marginalized populations. As such, even under the ideal sampling assumptions, the performance of RDS is restricted by the underlying social network: if the network is divided into communities that are weakly connected to each other, then RDS is likely to oversample one of these communities. In order to diminish the "referral bottlenecks" between communities, we propose anti-cluster RDS (AC-RDS), an adjustment to the standard RDS implementation. Using a standard model in the RDS literature, namely, a Markov process on the social network that is indexed by a tree, we construct and study the Markov transition matrix for AC-RDS. We show that if the underlying network is generated from the Stochastic Blockmodel with equal block sizes, then the transition matrix for AC-RDS has a larger spectral gap and consequently faster mixing properties than the standard random walk model for RDS. In addition, we show that AC-RDS reduces the covariance of the samples in the referral tree compared to the standard RDS and consequently leads to a smaller variance and design effect. We confirm the effectiveness of the new design using both the Add-Health networks and simulated networks.