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| Respondent-Driven Sampling× | 調査標本重み付けとキャリブレーション× | |
|---|---|---|
| 分野 | 調査方法論 | 調査方法論 |
| 系統 | Process / pipeline | Process / pipeline |
| 提唱年≠ | 1997 | 2010 |
| 提唱者≠ | Douglas Heckathorn | Sharon Lohr |
| 種類≠ | Probabilistic chain-referral sampling design | Estimation adjustment procedure |
| 原典≠ | Heckathorn, 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 |
| 別名 | Chain-Referral Sampling, Peer-Referral Sampling, Network-Based Sampling, Katılımcı Güdümlü Örnekleme | Survey Calibration, Post-Stratification Weighting, Raking Adjustment, Ağırlıklandırma (Anket) |
| 関連 | 3 | 3 |
| 概要≠ | Respondent-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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