Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Klusterirandomoitu monihaarainen koe× | Faktoriaalinen satunnaistettu kontrolloitu koe× | |
|---|---|---|
| Tieteenala | Koesuunnittelu | Koesuunnittelu |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1990s–2000s (systematic formalization) | 1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization) |
| Kehittäjä≠ | Building on cluster randomization (Donner & Klar) and multi-arm trial methods developed in clinical and public health research | R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014) |
| Tyyppi≠ | Experimental design | Experimental trial design |
| Alkuperäislähde≠ | Donner, A., & Klar, N. (2000). Design and Analysis of Cluster Randomization Trials in Health Research. Arnold. ISBN: 978-0340691533 | Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014). Factorial experiments: Efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47(4), 498–504. DOI ↗ |
| Rinnakkaisnimet | multi-arm cluster RCT, cluster-randomized multi-group trial, multi-arm group-randomized trial, CRCT multi-arm | Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | A cluster randomized multi-arm experiment assigns intact groups — such as schools, clinics, or villages — rather than individuals to three or more experimental conditions simultaneously. Randomization occurs at the cluster level to prevent contamination between arms, while the multi-arm structure allows simultaneous evaluation of several interventions against a common control or each other, improving efficiency over a series of two-arm studies. | A factorial randomized controlled trial (factorial RCT) is an experimental design in which participants are randomly assigned to every possible combination of two or more independent factors (treatments or intervention components) simultaneously. This allows researchers to estimate the main effect of each factor and their interactions within a single, efficient trial, rather than running separate experiments for each factor. |
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