Duke Network Analysis Center Seminar: Using Multiple Data Sources to Evaluate Respondent-driven Sampling (RDS) & Explore New Approaches to Studying Hidden & Hard-to-Reach Populations

This presentation summarizes a set of recent papers on RDS and discusses new approaches to the study of hidden populations. We summarize work from an empirical RDS study conducted among female sex workers in China. In this newer work we evaluate the bias and efficiency of sample level mean statistics across multiple RDS estimators, substantive variables of interest, and seeding and recruitment assumptions. We also discuss recent simulation work that explores biases in variance estimation in RDS. Finally, we conclude with a discussion of newly proposed approaches to surveying hidden populations and preliminary results from a survey conducted this summer using such methods in Tanzania.