השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| ניסוי פקטוריאלי חלקי פרגמטי× | תכנון ריבוע לטיני וריבוע יווני-לטיני× | |
|---|---|---|
| תחום | תכנון ניסויים | תכנון ניסויים |
| משפחה≠ | Process / pipeline | Hypothesis test |
| שנת המקור≠ | Fractional factorial designs: 1940s–1950s; pragmatic application: 2000s–2010s | 1935 |
| הוגה השיטה≠ | Building on Fisher (1935); pragmatic adaptation by Collins, Murphy & Strecher (2007) via MOST framework | Ronald A. Fisher |
| סוג≠ | Experimental design | Parametric blocked ANOVA |
| מקור מכונן≠ | 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 |
| כינויים≠ | pragmatic FFE, fractional factorial trial, pragmatic factorial design, FFD in pragmatic settings | Latin Square, Greco-Latin Square, Latin Kare ve Greco-Latin Kare Deseni |
| קשורות≠ | 4 | 5 |
| תקציר≠ | 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. |
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