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	Robust Model Testing Research	Analiza ścieżek
Dziedzina	Projektowanie badań	Statystyka
Rodzina	Process / pipeline	Latent structure
Rok powstania	1988–1998	1921
Twórca	Albert Satorra & Peter M. Bentler; Ke-Hai Yuan	Sewall Wright
Typ	Quantitative model-testing research design with robust estimation	Causal / mediation model
Źródło pierwotne	Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage.	Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.
Inne nazwy	robust SEM, robust structural model testing, robust fit evaluation, robust model evaluation research	PA, path coefficient analysis, observed-variable SEM, causal path modeling
Pokrewne	6	5
Podsumowanie	Robust model testing research applies structural or path models to data while explicitly accounting for violations of multivariate normality and other distributional assumptions. Rather than discarding non-normal data or forcing transformations, it uses corrected estimators — most notably the Satorra-Bentler scaled chi-square and Yuan-Bentler robust standard errors — to produce trustworthy fit indices and parameter estimates even when classical maximum likelihood assumptions are breached.	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.