Comparer des méthodes

	Expérience factorielle complète par croisement	Expérience factorielle croisée
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	Mid-to-late 20th century (crossover trials formalised ~1960s–1980s; full factorial DoE from Fisher ~1935)	1920s–1960s (synthesis of factorial and crossover traditions)
Auteur d'origine	Developed within the design-of-experiments tradition (R. A. Fisher and successors); crossover adaptation formalised by B. Jones and M. G. Kenward	R. A. Fisher (factorial principles, 1920s); crossover integration developed in biostatistics through mid-20th century
Type	Within-subject full factorial experimental design	Experimental design
Source fondatrice	Jones, B., & Kenward, M. G. (2003). Design and Analysis of Cross-Over Trials (2nd ed.). Chapman and Hall/CRC. ISBN: 978-1584883429	Jones, B., & Kenward, M. G. (2014). Design and Analysis of Cross-Over Trials (3rd ed.). Chapman and Hall/CRC. ISBN: 978-1439861424
Alias	within-subject full factorial design, repeated-measures full factorial experiment, crossover factorial trial, full factorial crossover design	within-subject factorial design, repeated-measures factorial experiment, factorial crossover trial, crossover factorial trial
Apparentées	6	5
Résumé	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.	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.