Vertaile menetelmiä
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| Osallistujapohjainen otanta× | Stratifioitu otanta× | Kyselypainotus ja kalibrointi× | |
|---|---|---|---|
| Tieteenala | Kyselytutkimuksen metodologia | Kyselytutkimuksen metodologia | Kyselytutkimuksen metodologia |
| Menetelmäperhe | Process / pipeline | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1997 | 1977 | 2010 |
| Kehittäjä≠ | Douglas Heckathorn | William G. Cochran | Sharon Lohr |
| Tyyppi≠ | Probabilistic chain-referral sampling design | Probability-based survey sampling design | Estimation adjustment procedure |
| Alkuperäislähde≠ | Heckathorn, D. D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44(2), 174–199. DOI ↗ | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7 | Lohr, S. L. (2010). Sampling: Design and Analysis (2nd ed.). Brooks/Cole. ISBN: 978-0-495-10527-5 |
| Rinnakkaisnimet | Chain-Referral Sampling, Peer-Referral Sampling, Network-Based Sampling, Katılımcı Güdümlü Örnekleme | Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme | Survey Calibration, Post-Stratification Weighting, Raking Adjustment, Ağırlıklandırma (Anket) |
| Liittyvät≠ | 3 | 2 | 3 |
| Tiivistelmä≠ | 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. | Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics. | 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. |
| ScholarGateAineisto ↗ |
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