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Respondent-driven sampling (RDS) is a popular method for samplinghard-to-survey populations that leverages social network connections throughpeer recruitment. While RDS is most frequently applied to estimate theprevalence of infections and risk behaviors of interest to public health, likeHIV/AIDS or condom use, it is rarely used to draw inferences about thestructural properties of social networks among such populations because it doesnot typically collect the necessary data. Drawing on recent advances incomputer science, we introduce a set of data collection instruments and RDSestimators for network clustering, an important topological property that hasbeen linked to a network's potential for diffusion of information, disease, andhealth behaviors. We use simulations to explore how these estimators,originally developed for random walk samples of computer networks, perform whenapplied to RDS samples with characteristics encountered in realistic fieldsettings that depart from random walks. In particular, we explore the effectsof multiple seeds, without vs. with replacement, branching chains, imperfectresponse rates, preferential recruitment, and misreporting of ties. We findthat clustering coefficient estimators retain desirable properties in RDSsamples. This paper takes an important step towards calculating networkcharacteristics using non-traditional sampling methods, and it expands RDS'spotential to tell researchers more about hidden populations and the socialfactors driving disease prevalence.

New Survey Questions and Estimators for Network Clustering with Respondent-Driven Sampling Data