Diagnostics for respondent-driven sampling

Krista J. Gile, Lisa G. Johnston and Matthew J. Salganik

Journal of the Royal Statistical Society Series A, 2015, vol. 178, issue 1, 241-269

Abstract: type="main" xml:id="rssa12059-abs-0001">

Respondent-driven sampling (RDS) is a widely used method for sampling from hard-to-reach human populations, especially populations at higher risk for human immunodeficiency virus or acquired immune deficiency syndrome. Data are collected through a peer referral process over social networks. RDS has proven practical for 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 partially beyond the control of the researcher and not fully observable. We introduce diagnostic tools for most of these assumptions and apply them in 12 high risk populations. These diagnostics empower researchers to understand their RDS data better and encourage future statistical research on RDS sampling and inference.

Date: 2015
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