Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Experimento Factorial Completo Pragmático× | Experimento Factorial Fraccionado× | |
|---|---|---|
| Campo | Diseño experimental | Diseño experimental |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1920s (factorial); 1967/2009 (pragmatic framework) | 1945 (Finney); broader development 1950s–1970s by Box, Hunter |
| Autor original≠ | Full factorial: R.A. Fisher (1920s); Pragmatic framing: Schwartz & Lellouch (1967), formalized by Thorpe et al. (2009) | D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work |
| Tipo≠ | Experimental design | Quantitative experimental design |
| Fuente seminal≠ | Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalmers, I. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464-475. 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 |
| Alias | pragmatic factorial trial, real-world full factorial design, effectiveness full factorial experiment, pragmatic 2^k experiment | fractional factorial design, FFD, 2^(k-p) design, fractional replication |
| Relacionados≠ | 6 | 4 |
| Resumen≠ | A pragmatic full factorial experiment combines the complete crossing of all factor levels (the full factorial structure) with the broad eligibility criteria, flexible delivery, and real-world conditions of a pragmatic trial. Every possible combination of factors is tested simultaneously, yielding both main effects and all interaction effects, while deliberately relaxing strict laboratory controls to reflect how interventions actually operate in practice. | 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. |
| ScholarGateConjunto de datos ↗ |
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