Unbiased estimation strategies for respondent driven sampling

Abstract

In this paper, we focus on respondent-driven sampling (RDS), which is a valuable survey methodology to estimate the size and the characteristics of hidden or hard-to-measure population groups. The RDS methodology makes it possible to gather information on these populations by exploiting the relationships between their components. However, RDS suffers from the lack of an estimation methodology that is sufficiently robust to accommodate the varying conditions under which it is applied. In this paper, we address the estimation problem of the RDS methodology and, by approaching it as a particular indirect sampling technique, we propose three unbiased estimation methods as possible solutions.