ESRA 2027 sessions by theme
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Respondent-Driven Sampling: Design, Error Sources, and Adjustments |
| Coordinator 1 | Dr Carina Cornesse (GESIS Leibniz Institute for the Social Sciences) |
| Coordinator 2 | Dr Mariel McKone Leonard (German Institute for Economic Research) |
| Coordinator 3 | Professor Olga Maslovskaya (University of Southampton) |
| Coordinator 4 | Dr Dina Neiger (Social Research Centre (SRC) Melbourne) |
| Coordinator 5 | Mr Curtis Jessop (National Centre for Social Research) |
Session Details
Respondent-Driven Sampling (RDS) has emerged as a methodology for studying hard-to-reach and hidden populations, from key populations in public health to marginalized or stigmatized groups in social research. However, the effectiveness of RDS depends on careful methodological choices, robust technical systems, and sophisticated approaches to error detection and adjustment. Our session seeks to advance the state-of-the-art by gathering expertise and innovation addressing the many challenges and opportunities in RDS research.
We particularly welcome research on the following topics:
- Designing RDS for diverse target populations exploring adaptations that increase network penetration, sample reach, and representativeness under varying social and cultural contexts. Presenters are encouraged to share empirical experiences and design strategies tailored to unique population structures and settings.
- Technical solutions and usability improvements that facilitate RDS implementation in particular on the web, including enhanced digital platforms for recruitment tracking, automation of coupon management, improved respondent interfaces, and innovative approaches to managing and documenting RDS waves.
- Incentive schemes and participant engagement measures, looking at the impact of various incentive models, communication strategies, and engagement tools on recruitment success.
- Sources of measurement error, nonresponse bias, and misrepresentation including the cumulative quantification of error, as well as statistical and methodological adjustments.
- Fraud detection and prevention, including verification procedures, anomaly detection, and strategies to minimize duplicate participation and deceptive reporting.
Both qualitative and quantitative work on these topics is invited. Research including experimental designs are especially welcome. With this session, we aim to foster a comprehensive dialogue on advancing RDS methodology and exploring its benefits and limitations.
