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January 16, 2026PLoS ONE3 citationsOpen Access

Comparing snowball sampling and RDS: A methodology and case study

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DKDongah KimKGKrista J. GileBMBradley Mathers

Key Points

  • This research aims to compare snowball sampling and Respondent Driven Sampling (RDS) to understand their effectiveness in sampling hard-to-reach populations.
  • Utilized data-based simulations to analyze outcomes from both sampling methods.
  • Investigated snowball sampling initiated from a health service.
  • Compared results from snowball sampling and RDS methodologies.
  • Identified potential similarities and differences in the outcomes from both methods.
  • Demonstrated that RDS better approximates probability sampling compared to snowball sampling.
  • Highlighted the applicability of simplified snowball sampling for monitoring when RDS is not feasible.

Abstract

Both snowball sampling and Respondent Driven Sampling (RDS) are used to sample hard-to-reach populations. Snowball sampling was initially developed as a probability sampling method, but in practice, it is widely used as a non-probabilistic sampling method. RDS was developed to address the limitations of snowball sampling and can be used to approximate a probability sampling method in practice. Therefore, RDS is often recommended for bio-behavioral surveys (BBS) for surveillance of HIV, viral hepatitis, and STIs among key populations. In some settings, simpler and cheaper monitoring are desired. WHO and UNAIDS are developing a simplified and rapid bio-behavioral survey methodology, a version of snowball sampling to use when RDS is infeasible. In this paper, we use data-based simulations to examine the potential similarities and differences between results from a snowball sample with recruitment initiated from a health service and samples recruited through RDS methodology.

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Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/6969d518940543b977709fa0https://doi.org/10.1371/journal.pone.0331666
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