Porównaj metody

	Dynamic Causal Modeling	Modelowanie równań strukturalnych
Dziedzina	Neuroobrazowanie	Statystyka w badaniach
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2003	1921
Twórca	Karl J. Friston	Sewall Wright
Typ	Causal modeling pipeline for neuroimaging	Method
Źródło pierwotne	Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302.	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	DCM, Dynamic Causal Model	SEM, path analysis, latent variable modeling, causal modeling
Pokrewne	2	3
Podsumowanie	Dynamic Causal Modeling (DCM) is a Bayesian framework for specifying and inverting generative models of brain connectivity from neuroimaging data. Introduced by Karl Friston and colleagues in 2003, DCM treats brain regions as dynamical systems and estimates effective connectivity by fitting observed fMRI time series to a biophysically plausible model of neuronal interactions.	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.