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Respondent-Driven Sampling×Vážení a kalibrace výběrových šetření×
OborMetodologie dotazníkových šetřeníMetodologie dotazníkových šetření
RodinaProcess / pipelineProcess / pipeline
Rok vzniku19972010
TvůrceDouglas HeckathornSharon Lohr
TypProbabilistic chain-referral sampling designEstimation adjustment procedure
Původní zdrojHeckathorn, D. D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44(2), 174–199. DOI ↗Lohr, S. L. (2010). Sampling: Design and Analysis (2nd ed.). Brooks/Cole. ISBN: 978-0-495-10527-5
Další názvyChain-Referral Sampling, Peer-Referral Sampling, Network-Based Sampling, Katılımcı Güdümlü ÖrneklemeSurvey Calibration, Post-Stratification Weighting, Raking Adjustment, Ağırlıklandırma (Anket)
Příbuzné33
ShrnutíRespondent-Driven Sampling (RDS) is a probabilistic chain-referral method designed to reach hidden or hard-to-reach populations that lack a sampling frame. Introduced by sociologist Douglas Heckathorn in 1997, RDS combines snowball recruitment with mathematical weighting based on participants' personal network sizes, allowing researchers to generate population-level estimates even when no complete membership list exists.Survey weighting is a statistical procedure that assigns a numeric weight to each sampled unit so that the weighted sample reproduces known population totals. Rooted in classical sampling theory and systematically synthesized by Sharon Lohr (2010), the approach corrects for unequal selection probabilities, unit nonresponse, and coverage gaps, producing estimates that are more representative of the target population than raw sample means or totals would be.
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ScholarGatePorovnat metody: Respondent-Driven Sampling · Survey Weighting. Získáno 2026-06-17 z https://scholargate.app/cs/compare