Porovnať metódy

	Pragmatický faktorový experiment	Plný faktoriálový experimentálny návrh
Odbor	Plánovanie experimentov	Plánovanie experimentov
Rodina	Process / pipeline	Hypothesis test
Rok vzniku	2000s–2010s (formal integration)	1926
Tvorca	Synthesized from pragmatic trial methodology (Schwartz & Lellouch, 1967) and factorial design principles (Fisher, 1935); formalized in clinical research contexts in the 2000s–2010s	R. A. Fisher
Typ	Experimental trial design	Parametric factorial experiment
Pôvodný zdroj	Loudon, K., Treweek, S., Sullivan, F., Donnan, P., Thorpe, K. E., & Zwarenstein, M. (2015). The PRECIS-2 tool: designing trials that are fit for purpose. BMJ, 350, h2147.	Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130
Ďalšie názvy	pragmatic factorial trial, pragmatic factorial RCT, real-world factorial design, PFE	factorial experiment, 2^k factorial, full factorial, Faktöriyel Deneme Deseni (Full Factorial, 2^k)
Príbuzné	3	5
Zhrnutie	A pragmatic factorial experiment combines two powerful methodological frameworks: the factorial experimental design — which tests multiple intervention components simultaneously — and the pragmatic trial orientation, which prioritizes real-world applicability, broad eligibility criteria, and flexible delivery conditions. The result is a design that efficiently evaluates which components of a complex intervention work, and whether they interact, while maintaining ecological validity for health, behavioral, and educational research.	A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured.