Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Experimento Fatorial Completo Duplo-Cego× | Experimento Fatorial Fracionado× | |
|---|---|---|
| Área | Delineamento experimental | Delineamento experimental |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1935 (factorial foundations, Fisher); double-blind combined application from 1950s onward | 1945 (Finney); broader development 1950s–1970s by Box, Hunter |
| Autor original≠ | Full factorial design: Ronald A. Fisher; double-blind masking: formalized in clinical research mid-20th century | D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work |
| Tipo≠ | Controlled experimental design with masking | Quantitative experimental design |
| Fonte seminal≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443 | 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 |
| Outros nomes | double-masked full factorial design, double-blind complete factorial experiment, blinded full factorial RCT, double-blind factorial trial | fractional factorial design, FFD, 2^(k-p) design, fractional replication |
| Relacionados | 4 | 4 |
| Resumo≠ | A double-blind full factorial experiment crosses every level of every independent variable to create all possible treatment combinations, while ensuring that neither participants nor outcome assessors know which condition each participant has been assigned to. This design simultaneously achieves comprehensive examination of main effects and all interactions, and protection against performance and detection bias through blinding — making it especially valuable in clinical, pharmacological, and behavioral research. | 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 dados ↗ |
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