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| 교차 완전 요인 실험× | 반복측정 분산분석× | |
|---|---|---|
| 분야≠ | 실험설계 | 통계학 |
| 계열≠ | Process / pipeline | Hypothesis test |
| 기원 연도≠ | Mid-to-late 20th century (crossover trials formalised ~1960s–1980s; full factorial DoE from Fisher ~1935) | 1992 |
| 창시자≠ | Developed within the design-of-experiments tradition (R. A. Fisher and successors); crossover adaptation formalised by B. Jones and M. G. Kenward | Girden (textbook treatment); Field (2013) |
| 유형≠ | Within-subject full factorial experimental design | Parametric within-subjects mean comparison |
| 원전≠ | Jones, B., & Kenward, M. G. (2003). Design and Analysis of Cross-Over Trials (2nd ed.). Chapman and Hall/CRC. ISBN: 978-1584883429 | Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE. ISBN: 978-1446249185 |
| 별칭 | within-subject full factorial design, repeated-measures full factorial experiment, crossover factorial trial, full factorial crossover design | within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA |
| 관련≠ | 6 | 4 |
| 요약≠ | 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. | Repeated-measures ANOVA is a parametric hypothesis test that compares three or more measurements taken from the same individuals — typically across time points or conditions — to decide whether their means differ. It extends one-way ANOVA to within-subjects designs, as treated in standard references such as Girden (1992) and Field (2013). |
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