The importance of measuring and accounting for potential biases in respondent-driven samples

Abby E. Rudolph, Crystal M. Fuller, Carl Latkin

Research output: Contribution to journalArticlepeer-review

Abstract

Respondent-driven sampling (RDS) is often viewed as a superior method for recruiting hard-to-reach populations disproportionately burdened with poor health outcomes. As an analytic approach, it has been praised for its ability to generate unbiased population estimates via post-stratified weights which account for non-random recruitment. However, population estimates generated with RDSAT (RDS Analysis Tool) are sensitive to variations in degree weights. Several assumptions are implicit in the degree weight and are not routinely assessed. Failure to meet these assumptions could result in inaccurate degree measures and consequently result in biased population estimates. We highlight potential biases associated with violating the assumptions implicit in degree weights for the RDSAT estimator and propose strategies to measure and possibly correct for biases in the analysis.

Original languageEnglish (US)
Pages (from-to)2244-2252
Number of pages9
JournalAIDS and behavior
Volume17
Issue number6
DOIs
StatePublished - Jul 2013

Keywords

  • Bias
  • Hidden populations
  • Respondent-driven sampling
  • Sampling weights

ASJC Scopus subject areas

  • Social Psychology
  • Public Health, Environmental and Occupational Health
  • Infectious Diseases

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