Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Crossover-ontwerp× | Split-Plot Experimenteel Ontwerp× | |
|---|---|---|
| Vakgebied | Experimenteel ontwerp | Experimenteel ontwerp |
| Familie | Hypothesis test | Hypothesis test |
| Jaar van ontstaan≠ | 1960 | 1935 |
| Grondlegger≠ | Early formalized in clinical research literature; widely used since mid-20th century | Frank Yates |
| Type≠ | Within-subject repeated-measures design | Parametric mixed-model ANOVA |
| Oorspronkelijke bron≠ | Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley. ISBN: 978-0471496533 | Yates, F. (1935). Complex Experiments. Supplement to the Journal of the Royal Statistical Society, 2(2), 181–247. DOI ↗ |
| Aliassen≠ | within-subject crossover, cross-over design, AB/BA design, Çapraz Desen (Crossover Design) | split-plot ANOVA, whole-plot sub-plot design, Bölünmüş Parsel Deseni (Split-Plot) |
| Verwant | 6 | 6 |
| Samenvatting≠ | A crossover design is an experimental design in which each participant receives all treatments under investigation, but in a different sequence and across separate time periods. Each subject thus acts as their own control, which substantially reduces between-subject variability and allows efficient treatment comparisons with smaller sample sizes. The approach has been central to clinical pharmacology and comparative research since the mid-20th century, with foundational methodology codified by Senn (2002) and Jones & Kenward (2014). | The split-plot design is a parametric experimental design that applies one factor to large whole plots and a second factor to subdivisions (sub-plots) within each whole plot. It was introduced by Frank Yates in 1935 to handle agricultural experiments where one factor — such as irrigation or tillage method — is difficult or impractical to change frequently, while a second factor can be varied more easily within the same plot. |
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