- By: | Edited by: Paul Atkinson, Sara Delamont, Alexandru Cernat, Joseph W. Sakshaug & Richard A.Williams &
- Publisher: SAGE Publications Ltd
- Publication year: 2020
- Online pub date:
- Discipline: Anthropology, Business and Management, Communication and Media Studies, Computer Science, Counseling and Psychotherapy, Criminology and Criminal Justice, Economics, Education, Engineering, Geography, Health, History, Marketing, Mathematics, Medicine, Nursing, Political Science and International Relations, Psychology, Social Policy and Public Policy, Science, Social Work, Sociology, Technology
- Methods: Respondent-driven sampling, Snowball sampling, Probability sampling
- Length: 10k+ Words
This entry responds to the ever-increasing demands for data on hard-to-sample populations for which there is no practical solution for sampling. Respondent-driven sampling (RDS) first appeared in the literature in 1997 as an alternative to probability sampling methods for recruiting rare populations. Since then, RDS has attracted vast interest and applied to numerous data collection activities targeting hard-to-reach and elusive populations from migrants to drug users to commercial sex workers around the world. Despite the popularity, the premise of RDS to be a valid tool for inferences is not widely known, and the reality of RDS field work is rarely reported in the literature. This entry provides an overview of RDS as a component in data collection methods from a survey methodology point of view rather than a sampling point of view, as the latter dominates the extant literature. For doing so, it draws on the literature as well as the authors’ own data collection experiences to empirically demonstrate points and offer practical options for analyzing RDS data.