  Respondent-driven sampling (RDS) is a popular method for sampling
hard-to-survey populations that leverages social network connections through
peer recruitment. While RDS is most frequently applied to estimate the
prevalence of infections and risk behaviors of interest to public health, like
HIV/AIDS or condom use, it is rarely used to draw inferences about the
structural properties of social networks among such populations because it does
not typically collect the necessary data. Drawing on recent advances in
computer science, we introduce a set of data collection instruments and RDS
estimators for network clustering, an important topological property that has
been linked to a network's potential for diffusion of information, disease, and
health behaviors. We use simulations to explore how these estimators,
originally developed for random walk samples of computer networks, perform when
applied to RDS samples with characteristics encountered in realistic field
settings that depart from random walks. In particular, we explore the effects
of multiple seeds, without vs. with replacement, branching chains, imperfect
response rates, preferential recruitment, and misreporting of ties. We find
that clustering coefficient estimators retain desirable properties in RDS
samples. This paper takes an important step towards calculating network
characteristics using non-traditional sampling methods, and it expands RDS's
potential to tell researchers more about hidden populations and the social
factors driving disease prevalence.
