Respondent-Driven Sampling: An Assessment of Current Methodology

TL;DR

This paper critically assesses Respondent-Driven Sampling (RDS), highlighting its reliance on strong assumptions and potential biases, and cautions users about its limitations in estimating hard-to-reach populations.

Contribution

The paper evaluates the sensitivities of RDS estimators to biases and assumptions, providing a critical assessment of its current methodology and limitations.

Findings

RDS estimates are highly sensitive to initial sample bias

Respondent behavior significantly affects sampling accuracy

Without-replacement sampling assumptions are often unrealistic

Abstract

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.