  Respondent-Driven Sampling (RDS) employs a variant of a link-tracing network
sampling strategy to collect data from hard-to-reach populations. By tracing
the links in the underlying social network, the process exploits the social
structure to expand the sample and reduce its dependence on the initial
(convenience) sample.
  The primary goal of RDS is typically to estimate population averages in the
hard-to-reach population. The current estimates make strong assumptions in
order to treat the data as a probability sample. In particular, we evaluate
three critical sensitivities of the estimators: to bias induced by the initial
sample, to uncontrollable features of respondent behavior, and to the
without-replacement structure of sampling.
  This paper sounds a cautionary note for the users of RDS. While current RDS
methodology is powerful and clever, the favorable statistical properties
claimed for the current estimates are shown to be heavily dependent on often
unrealistic assumptions.
