Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Expérience factorielle fractionnaire randomisée par grappes× | Expérience factorielle fractionnaire× | |
|---|---|---|
| Domaine | Plans d'expériences | Plans d'expériences |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1950s (fractional factorial); 1980s-1990s (cluster-randomized extensions) | 1945 (Finney); broader development 1950s–1970s by Box, Hunter |
| Auteur d'origine≠ | Box, Hunter & Hunter (fractional factorial foundations); Murray & colleagues (group-randomized trial methodology) | D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work |
| Type≠ | Experimental design (compound) | Quantitative experimental design |
| Source fondatrice≠ | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130 | 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 |
| Alias | CR-FFE, cluster-randomized fractional factorial design, group-randomized fractional factorial trial, CRFFD | fractional factorial design, FFD, 2^(k-p) design, fractional replication |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | A cluster-randomized fractional factorial experiment combines two design principles: randomization is applied to intact groups (clusters such as schools, clinics, or communities) rather than individuals, and only a carefully chosen fraction of all possible factor-level combinations is tested. This pairing makes it practical to screen or evaluate multiple intervention components simultaneously in settings where individual randomization is infeasible, while keeping the number of required clusters manageable. | 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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