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| Eksperyment losowy kontrolowany z czynnikami× | Ułamkowy eksperyment czynnikowy× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization) | 1945 (Finney); broader development 1950s–1970s by Box, Hunter |
| Twórca≠ | R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014) | D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work |
| Typ≠ | Experimental trial design | Quantitative experimental design |
| Źródło pierwotne≠ | 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 ↗ | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130 |
| Inne nazwy | Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization | fractional factorial design, FFD, 2^(k-p) design, fractional replication |
| Pokrewne≠ | 6 | 4 |
| Podsumowanie≠ | 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. | A fractional factorial experiment is a resource-efficient experimental design that tests only a carefully chosen fraction of all possible factor-level combinations. By exploiting the principle that high-order interactions are usually negligible, it identifies the main effects and low-order interactions of k factors using far fewer runs than a full factorial design — making it the workhorse of industrial and engineering screening experiments. |
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