Salīdzināt metodes

	Faktoriālais vienas personas eksperimentālais dizains	Faktoriālais randomizētais kontrolētais pētījums
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1970s–1980s	1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)
Autors	Applied behavior analysis tradition; systematized in Barlow & Hersen (1984) and Kazdin (1982)	R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)
Tips	Experimental single-subject design with multiple independent variables	Experimental trial design
Pirmavots	Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press. ISBN: 978-0195341881	Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014). Factorial experiments: Efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47(4), 498–504.
Citi nosaukumi	factorial SCED, factorial single-case design, factorial N-of-1 design, factorial within-subject experimental design	Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization
Saistītās	6	6
Kopsavilkums	A factorial single-subject experimental design applies the logic of factorial experiments — manipulating two or more independent variables simultaneously to study main effects and interactions — within a single-subject (N=1 or small N) repeated-measures framework. Instead of comparing groups, the same individual serves as their own control across systematically varied conditions, enabling fine-grained analysis of how multiple treatment components combine to influence behavior or clinical outcomes.	A factorial randomized controlled trial (factorial RCT) is an experimental design in which participants are randomly assigned to every possible combination of two or more independent factors (treatments or intervention components) simultaneously. This allows researchers to estimate the main effect of each factor and their interactions within a single, efficient trial, rather than running separate experiments for each factor.