Módszerek összehasonlítása

	Faktoriális természetes kísérlet – Több tényezős exogén variációs dizájn	Faktoriális randomizált kontrollált vizsgálat
Tudományterület	Kísérlettervezés	Kísérlettervezés
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	1920s (factorial origins, Fisher); natural experiment formalization 1990s–2000s; factorial natural experiment usage widespread 2000s–present	1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)
Megalkotó	Extension of natural experiment tradition (Dunning, Angrist & Pischke) combined with factorial design logic (Fisher)	R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)
Típus	Quasi-experimental research design	Experimental trial design
Alapmű	Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press. ISBN: 978-1107698000	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.
Alternatív nevek	factorial quasi-experiment, multi-factor natural experiment, factorial exogenous variation design	Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization
Kapcsolódó	4	6
Összefoglaló	A factorial natural experiment exploits naturally occurring exogenous variation across two or more factors simultaneously, allowing researchers to estimate main effects and interactions without random assignment. Natural events, policy changes, or institutional rules create treatment conditions that approximate a factorial structure, enabling causal inference in observational settings where controlled experimentation is infeasible or unethical.	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.