Porównaj metody

	Eksploracyjna analiza czynnikowa dla danych porządkowych	Modelowanie równań strukturalnych
Dziedzina	Psychometria	Statystyka w badaniach
Rodzina	Latent structure	Process / pipeline
Rok powstania	1978–1984	1921
Twórca	Bengt Muthén	Sewall Wright
Typ	Latent variable / dimension reduction	Method
Źródło pierwotne	Flora, D. B. & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491.	Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.
Inne nazwy	ordinal factor analysis, polychoric EFA, categorical EFA, EFA for ordinal data	SEM, path analysis, latent variable modeling, causal modeling
Pokrewne	5	3
Podsumowanie	Ordinal exploratory factor analysis discovers latent factors underlying a set of ordinal items — typically Likert scales — by computing polychoric correlations among the items and then applying a weighted least squares estimator. It avoids the distortions that arise when continuous EFA methods are naively applied to ordered categorical responses.	Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.