Salīdzināt metodes

	Pilna faktoriālā eksperimenta krustojums	ANOVA ar atkārtotiem mērījumiem
Nozare	Eksperimentu plānošana	Statistika
Saime	Process / pipeline	Hypothesis test
Izcelsmes gads	Mid-to-late 20th century (crossover trials formalised ~1960s–1980s; full factorial DoE from Fisher ~1935)	1992
Autors	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)
Tips	Within-subject full factorial experimental design	Parametric within-subjects mean comparison
Pirmavots	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
Citi nosaukumi	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
Saistītās	6	4
Kopsavilkums	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).