Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Mukautuva poikkileikkaus-epidemiologinen tutkimus× | Adaptive Sampling× | |
|---|---|---|
| Tieteenala≠ | Epidemiologia | Kyselytutkimuksen metodologia |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1990s–2000s (formalization of adaptive elements in observational surveys) | 1990 |
| Kehittäjä≠ | Conceptual synthesis of adaptive design methods (Wald, 1947; Bauer & Kohne, 1994) with classical cross-sectional epidemiology (MacMahon & Pugh, 1960s) | Steven Thompson |
| Tyyppi≠ | Observational epidemiological study design | Probability-based adaptive design |
| Alkuperäislähde≠ | Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press. ISBN: 978-0195083439 | Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059. DOI ↗ |
| Rinnakkaisnimet | adaptive cross-sectional survey, adaptive prevalence study, adaptive epidemiological survey design, adaptive population cross-section | Adaptive Cluster Sampling, Sequential Adaptive Sampling, Network Sampling, Adaptif Küme Örneklemesi |
| Liittyvät≠ | 2 | 3 |
| Tiivistelmä≠ | An adaptive cross-sectional epidemiological study combines the core logic of a cross-sectional survey — measuring exposures and outcomes simultaneously in a defined population at one point in time — with pre-specified adaptive rules that allow modifications to sampling strategy, sample size, or subgroup allocation based on accumulating interim data. The approach preserves the efficiency and speed of a standard cross-sectional design while improving precision for rare exposures or heterogeneous populations by redirecting sampling resources in real time. | Adaptive Cluster Sampling (ACS) is a probability-based survey design introduced by Steven K. Thompson in 1990 for estimating the abundance or total of rare, clustered populations. Starting from an initial random sample, the design adaptively adds neighboring units whenever a sampled unit satisfies a predefined condition—such as exceeding a count threshold—thereby concentrating sampling effort exactly where the population of interest occurs. It is most appropriate for ecologists, epidemiologists, and social scientists studying geographically or socially clustered rare phenomena. |
| ScholarGateAineisto ↗ |
|
|