Estimating network clustering with a latent space model from Respondent-Driven Sampling data

Thomas M. Menino Convention & Exhibition Center 

The social network structure of hidden and hard-to-reach populations can have important implications for epidemiology and public health. However, collecting full or even moderately dense network data is typically infeasible due to the lack of a sampling frame, privacy concerns, and limited resources. Respondent-Driven Sampling (RDS), which leverages a chain-referral pattern, is widely used in these cases, but standard RDS data provide only tree-structured network data. To address this challenge, we incorporated a recently developed token-based strategy that supplements coupon referral with token distribution, yielding additional observed ties. Inspired by work using aggregated relational data (ARD), we propose a latent space model to effectively describe the network structure. We use the fitted model to predict unobserved ties and estimate network statistics from RDS data, with particular focus on network clustering, an important network feature that cannot be recovered from standard RDS data. This work is motivated by and applied to a rich dataset of RDS samples collected among People Who Inject Drugs across 17 sites in Kenya in the TLC-IDU study (NCT01557998).

Respondent-Driven Sampling

RDS

Hard-to-reach population

Social networks

Network clustering

Latent space model 

Survey Research Methods Section