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| Експеримент с кръстосано дробно факториално планиране× | Дробен факториален експеримент× | |
|---|---|---|
| Област | Планиране на експеримента | Планиране на експеримента |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1950s–1970s (fractional factorial from 1940s; crossover integration from 1960s–1970s) | 1945 (Finney); broader development 1950s–1970s by Box, Hunter |
| Създател≠ | Box, Hunter & Hunter (fractional factorial); Senn & Williams (crossover integration) | D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work |
| Тип≠ | Within-subject multi-factor experimental design | Quantitative experimental design |
| Основополагащ източник≠ | Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley. ISBN: 978-0471496533 | 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 |
| Други названия | crossover FF design, within-subject fractional factorial, repeated-measures fractional factorial, crossover FFE | fractional factorial design, FFD, 2^(k-p) design, fractional replication |
| Свързани≠ | 5 | 4 |
| Резюме≠ | A crossover fractional factorial experiment is a within-subject design in which each participant receives a strategically chosen subset of all possible factor-level combinations in a defined sequence, with washout periods between treatment periods. By combining the run-economy of fractional factorial designs with the within-subject efficiency of crossover designs, it allows estimation of main effects and selected interactions while controlling for between-subject variability using far fewer participants and experimental runs than a full factorial crossover. | 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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