Diagnostics for Respondent-driven Sampling

TL;DR

This paper introduces diagnostic tools for assessing the assumptions underlying respondent-driven sampling (RDS), improving data validity and interpretation in studies of hard-to-reach populations at risk for HIV/AIDS.

Contribution

The paper develops and applies diagnostic tools to evaluate key assumptions in RDS, aiding researchers in understanding their data and promoting further statistical research.

Findings

Diagnostics effectively identify assumption violations in RDS data

Application to case studies demonstrates practical utility

Tools enhance confidence in RDS-based inferences

Abstract

Respondent-driven sampling (RDS) is a widely used method for sampling from hard-to-reach human populations, especially groups most at-risk for HIV/AIDS. Data are collected through a peer-referral process in which current sample members harness existing social networks to recruit additional sample members. RDS has proven to be a practical method of data collection in many difficult settings and has been adopted by leading public health organizations around the world. Unfortunately, inference from RDS data requires many strong assumptions because the sampling design is not fully known and is partially beyond the control of the researcher. In this paper, we introduce diagnostic tools for most of the assumptions underlying RDS inference. We also apply these diagnostics in a case study of 12 populations at increased risk for HIV/AIDS. We developed these diagnostics to enable RDS researchers to better understand their data and to encourage future statistical research on RDS.