Abstract
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 language | English |
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Article number | e0270673 |
Journal | PLoS ONE |
Volume | 17 |
Issue number | 7 |
DOIs | |
Publication status | Published - 21 Jul 2022 |
Keywords
- Bias
- HIV Infections/epidemiology
- Humans
- Research Design
- Sampling Studies
- Smartphone
- Surveys and Questionnaires
ASJC Scopus subject areas
- General