Salīdzināt metodes

	Eksperimentālais dizains ar krustojumu un faktoriālo struktūru	ANOVA ar atkārtotiem mērījumiem
Nozare	Eksperimentu plānošana	Statistika
Saime	Process / pipeline	Hypothesis test
Izcelsmes gads	1920s–1960s (synthesis of factorial and crossover traditions)	1992
Autors	R. A. Fisher (factorial principles, 1920s); crossover integration developed in biostatistics through mid-20th century	Girden (textbook treatment); Field (2013)
Tips	Experimental design	Parametric within-subjects mean comparison
Pirmavots	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
Citi nosaukumi	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
Saistītās	5	4
Kopsavilkums	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).