Diagnostics for respondent-driven sampling

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

Research output: Contribution to journalArticle

113 Scopus citations

Abstract

Summary: 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.

Original languageEnglish (US)
Pages (from-to)241-269
Number of pages29
JournalJournal of the Royal Statistical Society. Series A: Statistics in Society
Volume178
Issue number1
DOIs
StatePublished - Jan 1 2015

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Keywords

  • Acquired immune deficiency syndrome
  • Diagnostics
  • Exploratory data analysis
  • Hard-to-reach populations
  • Human immunodeficiency virus
  • Link tracing sampling
  • Non-ignorable design
  • Respondent-driven sampling
  • Social networks
  • Survey sampling

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