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| Crossover-fraktionielles faktorielles Experiment× | Vollfaktorielle Versuchsplanung× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1950s–1970s (fractional factorial from 1940s; crossover integration from 1960s–1970s) | 1926 (Fisher's foundational paper); codified by the 1950s–1960s |
| Urheber≠ | Box, Hunter & Hunter (fractional factorial); Senn & Williams (crossover integration) | Ronald A. Fisher |
| Typ≠ | Within-subject multi-factor experimental design | Experimental design |
| Wegweisende Quelle≠ | 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 |
| Aliasnamen | crossover FF design, within-subject fractional factorial, repeated-measures fractional factorial, crossover FFE | full factorial design, complete factorial design, 2^k factorial design, FFD |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | 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 full factorial experiment runs every possible combination of all chosen factor levels, making it the gold standard for simultaneously estimating main effects, two-way interactions, and higher-order interactions among multiple independent variables. Introduced through Ronald Fisher's foundational work on factorial designs in the 1920s and systematised by Box, Hunter, and Montgomery, it provides complete information about how factors act individually and in combination on an outcome. |
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