Porovnat metody

	Vícúrovňové modelování	Analýza rozptylu (ANOVA)	Logistická regrese	Modelování strukturálních rovnic
Obor	Statistika ve výzkumu	Statistika ve výzkumu	Statistika ve výzkumu	Statistika ve výzkumu
Rodina	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Rok vzniku	1992	1925	1958	1921
Tvůrce	Anthony Bryk and Stephen Raudenbush	Ronald A. Fisher	David Roxbee Cox	Sewall Wright
Typ	Method	Method	Method	Method
Původní zdroj	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	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.
Další názvy	HLM, mixed-effects models, random effects models, MLM	ANOVA, F-test	logit model, binomial logistic regression, LR	SEM, path analysis, latent variable modeling, causal modeling
Příbuzné	3	4	3	3
Shrnutí	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.	ANOVA is a parametric statistical method developed by Ronald A. Fisher in 1925 that tests whether means differ significantly across three or more independent groups. By partitioning total variance into between-group and within-group components, ANOVA determines whether observed differences are likely due to treatment effects or random variation, making it fundamental to comparative research across medicine, psychology, agriculture, and engineering.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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.