Challenges to Recruiting Representative Samples of Female Sex Workers in China Using Respondent Driven Sampling: How Much of the Network Do We See?
Giovanna Merli, Duke University
James Moody, Duke University
Jeff Smith, Duke University
Jing Li, Duke University
Sharon Weir, University of North Carolina at Chapel Hill
Xiangsheng Chen, National Center for STD Control, China
We explore the network coverage of a Respondent Driven Sampling (RDS) sample of female sex workers (FSW) in China as part of an effort to evaluate RDS' claim of population representation with empirical data. We take advantage of unique information on the social networks of FSW obtained from two overlapping studies of FSWs --RDS and a venue-based sampling approach (PLACE) -- and use an exponential random graph modeling (ERGM) framework from local networks to construct the likely network from which our observed RDS is drawn. We then run recruitment chains over this simulated population and produce a sample with characteristics consistent with the observed RDS. We estimate population coverage rates by comparing population proportions and RDS sample proportions. We discuss the results in light of (a) potential estimation improvements implicit in network information, (b) strategies for improving coverage rates, and (c) multiple sources of potential variability in coverage.
Presented in Session 11: Statistical, Spatial and Network Methods