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	Robust Model Testing Research	Badanie testujące model
Dziedzina	Projektowanie badań	Projektowanie badań
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1988–1998	1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s
Twórca	Albert Satorra & Peter M. Bentler; Ke-Hai Yuan	Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition
Typ	Quantitative model-testing research design with robust estimation	Confirmatory quantitative research design
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.	Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344
Inne nazwy	robust SEM, robust structural model testing, robust fit evaluation, robust model evaluation research	model-based research, structural model testing, theory-testing research, MTR
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.	Model testing research is a confirmatory quantitative design in which the researcher specifies a theoretical model — depicting hypothesized relationships among constructs — and then tests how well that model fits empirical data. Drawing primarily on structural equation modeling (SEM) and confirmatory factor analysis (CFA), it evaluates whether the data-implied covariance structure is consistent with the theoretically derived one, yielding fit indices that indicate model-data correspondence.