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Using geographical data and rolling statistics for diagnostics of respondent-driven sampling.

Brian Kim1, Moses Ogwal2, Enos Sande3

  • 1Joint Program in Survey Methodology, University of Maryland, 1218 LeFrak Hall, 7251 Preinkert Dr., College Park, MD 20742, USA.

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Summary
This summary is machine-generated.

Respondent-driven sampling (RDS) helps survey hard-to-reach populations. New geographical diagnostics assess RDS assumptions, revealing convergence issues and reach in Kampala, Uganda, for key populations.

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Area of Science:

  • Epidemiology
  • Social Sciences
  • Public Health

Background:

  • Respondent-driven sampling (RDS) is crucial for surveying populations lacking a sampling frame, such as key populations.
  • Traditional sampling methods are often inefficient for these groups, hindering essential public health research.
  • Concerns exist regarding the practical application and underlying assumptions of RDS.

Purpose of the Study:

  • To develop and evaluate novel diagnostics for assessing Respondent-driven sampling (RDS) assumptions.
  • To utilize geographical data for evaluating RDS convergence and reach.
  • To identify potential limitations in RDS implementation within key populations.

Main Methods:

  • Development of diagnostics leveraging geographical data.
  • Application of diagnostics to RDS surveys among female sex workers and men who have sex with men in Kampala, Uganda.
  • Analysis of survey data to assess convergence and geographical reach of the sampling method.

Main Results:

  • The developed geographical diagnostics successfully identified issues with RDS convergence.
  • The study characterized the geographical reach of RDS in the sampled populations.
  • Findings highlight potential challenges in applying RDS assumptions in real-world settings.

Conclusions:

  • Geographical diagnostics offer a valuable tool for assessing the validity of Respondent-driven sampling (RDS).
  • The study demonstrates the utility of these diagnostics in identifying practical limitations of RDS.
  • These findings can inform improvements in RDS methodology for key population research.