Módszerek összehasonlítása

	Multivariáns modelltesztelési kutatás	Útmutató elemzés
Tudományterület	Kutatástervezés	Statisztika
Módszercsalád	Process / pipeline	Latent structure
Keletkezés éve	1970s–1980s (multivariate model testing as a distinct approach)	1921
Megalkotó	Karl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis)	Sewall Wright
Típus	Quantitative confirmatory research design	Causal / mediation model
Alapmű	Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541	Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.
Alternatív nevek	multivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT research	PA, path coefficient analysis, observed-variable SEM, causal path modeling
Kapcsolódó	5	5
Összefoglaló	Multivariate model testing research is a confirmatory quantitative design in which a theoretically derived model involving multiple variables and their interrelationships is formally tested against empirical data. Rather than exploring patterns inductively, the researcher specifies a model a priori — capturing hypothesized directional paths, latent constructs, or covariance structures — and then evaluates how well this model reproduces the observed data using techniques such as structural equation modeling, confirmatory factor analysis, or multivariate path analysis.	Path analysis tests a researcher-specified causal diagram among observed variables by decomposing their intercorrelations into direct effects, indirect (mediated) effects, and spurious associations. Developed by Sewall Wright in 1921, it is the observed-variable special case of structural equation modeling and remains a standard tool for theory-driven multivariate causal inference.