Smartphone-based Respondent Driven Sampling (RDS): A methodological advance in surveying small or 'hard-to-reach' populations

Filip Lukasz Sosenko, Glen Bramley

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
76 Downloads (Pure)


Producing statistically robust profiles of small or 'hard-to-reach' populations has always been a challenge for researchers. Since surveying the wider population in order to capture a large enough sample of cases is usually too costly or impractical, researchers have been opting for 'snowballing' or 'time-location sampling'. The former does not allow for claims to representativeness, and the latter struggles with under-coverage and estimating confidence intervals. Respondent Driven Sampling (RDS) is a method that combines snowballing sampling with an analytical algorithm that corrects for biases that arise in snowballing. For all its advantages, a major weakness of RDS has been around data collection. Traditionally done on-site, the process is costly and lengthy. When done online, it is cheaper and faster but under a serious threat from fraud, compromising data quality and validity of findings. This paper describes a real-life application of a RDS data collection system that maximizes fraud prevention while still benefiting from low cost and speedy data collection.

Original languageEnglish
Article numbere0270673
JournalPLoS ONE
Issue number7
Publication statusPublished - 21 Jul 2022


  • Bias
  • HIV Infections/epidemiology
  • Humans
  • Research Design
  • Sampling Studies
  • Smartphone
  • Surveys and Questionnaires

ASJC Scopus subject areas

  • General


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