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| Esperimento Fattoriale Cross-Over× | ANOVA per misure ripetute× | |
|---|---|---|
| Campo≠ | Disegno sperimentale | Statistica |
| Famiglia≠ | Process / pipeline | Hypothesis test |
| Anno di origine≠ | 1920s–1960s (synthesis of factorial and crossover traditions) | 1992 |
| Ideatore≠ | R. A. Fisher (factorial principles, 1920s); crossover integration developed in biostatistics through mid-20th century | Girden (textbook treatment); Field (2013) |
| Tipo≠ | Experimental design | Parametric within-subjects mean comparison |
| Fonte seminale≠ | Jones, B., & Kenward, M. G. (2014). Design and Analysis of Cross-Over Trials (3rd ed.). Chapman and Hall/CRC. ISBN: 978-1439861424 | Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE. ISBN: 978-1446249185 |
| Alias | within-subject factorial design, repeated-measures factorial experiment, factorial crossover trial, crossover factorial trial | within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | A crossover factorial experiment combines two powerful design principles: factorial structure, which studies multiple factors and their interactions simultaneously, and crossover structure, in which each participant receives more than one treatment combination across sequential periods. By serving as their own control, participants reduce between-subject variability, improving statistical power while also revealing how different factor levels interact within the same individual. | 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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