  Respondent-Driven Sampling (RDS) is a variant of link-tracing, a sampling
technique for surveying hard-to-reach communities that takes advantage of
community members' social networks to reach potential participants. As a
network-based sampling method, RDS is faced with the fundamental problem of
sampling from population networks where features such as homophily (the
tendency for individuals with similar traits to share social ties) and
differential activity (the ratio of the average number of connections by
attribute) are sensitive to the choice of a sampling method. Though not clearly
described in the RDS literature, many simple methods exist to generate
simulated RDS data, with specific levels of network features, where the focus
is on estimating simple estimands. However, the accuracy of these methods in
their abilities to consistently recover those targeted network features remains
unclear. This is also motivated by recent findings that some population network
parameters (e.g.~homophily) cannot be consistently estimated from the RDS data
alone \citep{Crawford17}.
  In this paper, we conduct a simulation study to assess the accuracy of
existing RDS simulation methods, in terms of their abilities to generate RDS
samples with the desired levels of two network parameters: homophily and
differential activity. The results show that (1) homophily cannot be
consistently estimated from simulated RDS samples and (2) differential activity
estimates are more precise when groups, defined by traits, are equally active
and equally represented in the population. We use this approach to mimic
features of the Engage Study, an RDS sample of gay, bisexual and other men who
have sex with men in Montreal.
