Diagnostics for respondent-driven sampling

• Published in: Journal of the Royal Statistical Society. Series A, Statistics in society Vol. 178; no. 1; pp. 241 - 269
• Main Authors: Gile, Krista J., Johnston, Lisa G., Salganik, Matthew J.
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.01.2015; John Wiley & Sons Ltd; Oxford University Press
• Subjects: Acquired immune deficiency syndrome; AIDS; Coupons; Data collection; Diagnostic software; Diagnostics; Diseases; Estimation bias; Estimators; Exploratory data analysis; Hard-to-reach populations; Health care industry; Health risk assessment; High risk; HIV; Human immunodeficiency virus; Inference; Link tracing sampling; Non-ignorable design; Organizations; Population; Population estimates; Populations; Public health; Respiratory distress syndrome; Respondent-driven sampling; Risk; Sampling; Sampling bias; Sampling techniques; Social networks; Statistical analysis; Statistics; Survey sampling; HIV/AIDS; diagnostics; hard-to-reach populations; respondent-driven sampling; exploratory data analysis; non-ignorable design; survey sampling; social networks; link-tracing sampling
• ISSN: 0964-1998; 1467-985X
• DOI: 10.1111/rssa.12059

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.