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| Прагматичен фракционен факторен експеримент× | Латински квадрат и Гръко-латински квадрат× | |
|---|---|---|
| Област | Планиране на експеримента | Планиране на експеримента |
| Семейство≠ | Process / pipeline | Hypothesis test |
| Година на възникване≠ | Fractional factorial designs: 1940s–1950s; pragmatic application: 2000s–2010s | 1935 |
| Създател≠ | Building on Fisher (1935); pragmatic adaptation by Collins, Murphy & Strecher (2007) via MOST framework | Ronald A. Fisher |
| Тип≠ | Experimental design | Parametric blocked ANOVA |
| Основополагащ източник≠ | Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): New methods for more potent eHealth interventions. American Journal of Preventive Medicine, 32(5S), S112–S118. DOI ↗ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443 |
| Други названия≠ | pragmatic FFE, fractional factorial trial, pragmatic factorial design, FFD in pragmatic settings | Latin Square, Greco-Latin Square, Latin Kare ve Greco-Latin Kare Deseni |
| Свързани≠ | 4 | 5 |
| Резюме≠ | A pragmatic fractional factorial experiment applies fractional factorial design principles to real-world or clinical intervention research, enabling simultaneous evaluation of multiple intervention components in a resource-efficient fraction of the full factorial runs. Popularised through the Multiphase Optimization Strategy (MOST), it identifies which components of a multi-component intervention contribute meaningfully to outcomes before a confirmatory randomized trial is conducted. | The Latin square design is a blocked experimental design that simultaneously controls two independent nuisance factors — the row block and the column block — so that each treatment appears exactly once in every row and every column of an n×n arrangement. Formalised by Ronald A. Fisher in his 1935 monograph The Design of Experiments, the design dramatically reduces experimental error by absorbing variation from two extraneous sources before the treatment effects are estimated. |
| ScholarGateНабор от данни ↗ |
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