Vertaile menetelmiä
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| Adaptiivinen kvoottaotanta – dynaaminen kvootanhallinta kyselytutkimuksessa× | Mukautuva ositettu otanta× | |
|---|---|---|
| Tieteenala | Kyselytutkimuksen metodologia | Kyselytutkimuksen metodologia |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 2000s (adaptive extension of quota principles) | 1990s (formal development from Thompson 1990 onward) |
| Kehittäjä≠ | Grounded in quota sampling (Quota sampling formalized early 20th century); adaptive extensions developed within responsive survey design frameworks (Groves & Heeringa, 2006) | Steven K. Thompson (adaptive sampling); allocation adaptations by Salehi, Seber, and others |
| Tyyppi≠ | Non-probability sampling with adaptive control | Probability-based adaptive sampling design |
| Alkuperäislähde≠ | Groves, R. M., & Heeringa, S. G. (2006). Responsive design for household surveys: Tools for actively controlling survey errors and costs. Journal of the Royal Statistical Society: Series A, 169(3), 439–457. DOI ↗ | Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059. DOI ↗ |
| Rinnakkaisnimet≠ | responsive quota sampling, dynamic quota sampling, iterative quota sampling | ASS, adaptive stratified design, stratified adaptive sampling, adaptive allocation stratified sampling |
| Liittyvät≠ | 3 | 6 |
| Tiivistelmä≠ | Adaptive quota sampling is a non-probability sampling approach that starts with predefined demographic or characteristic-based quotas and then adjusts those quotas during data collection in response to emerging response patterns, nonresponse trends, or representativeness concerns. By treating the sampling process as iterative rather than fixed, it allows researchers to correct imbalances in real time and improve the final sample composition without restarting data collection from scratch. | Adaptive stratified sampling divides the population into strata and then applies an adaptive rule within each stratum: whenever an initially selected unit satisfies a pre-specified condition (e.g., a rare species is found, a variable exceeds a threshold), neighboring or related units are added to the sample. This combines the variance-reduction power of stratification with the ability to concentrate sampling effort where the phenomenon of interest is actually present. |
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