A Novel Estimate for the Respondent-Driven Sampling Methods: A Resampling Approach

Oregon Convention Center 

Respondent-driven sampling (RDS) and RDS-like methods, such as Link-Tracing Sampling and Network Sampling, have been widely used in studies of hidden or hard-to-reach populations over the past decade. However, little or no literature has addressed the issue of the violations of RDS or network sampling assumptions and data deficiencies, a challenge that are frequently encountered during RDS implementation due to the uncertainties of fieldwork. To this end, we present an empirical application of a novel estimate for the RDS or RDS-like sampling methods called new estimates for network sampling (NE4NS). It is resampling based, free of RDS and model-based assumptions, as opposed to the conventional RDS estimate in which the Volz-Heckathorn (VH) weighting scheme relies on the self-reported network sizes. The new and the conventional RDS estimations were applied to a sex trafficking prevalence study in Senegal, one of our ongoing projects on which the RDS method was used and the problem of insufficient sample size was faced. As a result, the new RDS estimate with the NE4NS strategy showed to be highly efficient and effective. Future application of the new RDS estimate is encouraged.

Respondent-Driven Sampling (RDS)

Network Sampling

New Estimates for Network Sampling (NE4NS)

Volz-Heckathorn (VH) Weighting Scheme

Hidden or Hard-to-Reach Population

Resampling Approach 

Survey Research Methods Section