手法を比較
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| クロスオーバー分割要因実験× | クロスオーバー完全実施実験× | |
|---|---|---|
| 分野 | 実験計画法 | 実験計画法 |
| 系統 | Process / pipeline | Process / pipeline |
| 提唱年≠ | 1950s–1970s (fractional factorial from 1940s; crossover integration from 1960s–1970s) | Mid-to-late 20th century (crossover trials formalised ~1960s–1980s; full factorial DoE from Fisher ~1935) |
| 提唱者≠ | Box, Hunter & Hunter (fractional factorial); Senn & Williams (crossover integration) | Developed within the design-of-experiments tradition (R. A. Fisher and successors); crossover adaptation formalised by B. Jones and M. G. Kenward |
| 種類≠ | Within-subject multi-factor experimental design | Within-subject full factorial experimental design |
| 原典≠ | Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley. ISBN: 978-0471496533 | Jones, B., & Kenward, M. G. (2003). Design and Analysis of Cross-Over Trials (2nd ed.). Chapman and Hall/CRC. ISBN: 978-1584883429 |
| 別名 | crossover FF design, within-subject fractional factorial, repeated-measures fractional factorial, crossover FFE | within-subject full factorial design, repeated-measures full factorial experiment, crossover factorial trial, full factorial crossover design |
| 関連≠ | 5 | 6 |
| 概要≠ | 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 crossover full factorial experiment combines the efficiency of a crossover (within-subject) design with the comprehensiveness of a full factorial design. Every participant receives all combinations of the factor levels across successive treatment periods, separated by washout intervals, allowing complete estimation of all main effects and interactions while using each participant as their own control. |
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