Porovnať metódy

	Výskum testovania hierarchických modelov	Modelovanie štruktúrnymi rovnicami
Odbor	Dizajn výskumu	Štatistika vo výskume
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994)	1921
Tvorca	Stephen Raudenbush and Anthony Bryk (HLM); extended to multilevel SEM by Bengt Muthen	Sewall Wright
Typ	Quantitative confirmatory research design	Method
Pôvodný zdroj	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	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.
Ďalšie názvy	multilevel model testing, hierarchical SEM, nested model testing, HLM model testing	SEM, path analysis, latent variable modeling, causal modeling
Príbuzné	5	3
Zhrnutie	Hierarchical model testing research is a quantitative design that evaluates theoretically derived models using data with a nested or clustered structure — for example, students within classrooms, employees within organisations, or patients within hospitals. It applies hierarchical linear models (HLM) or multilevel structural equation models (ML-SEM) to test whether a proposed set of relationships holds after properly accounting for the non-independence introduced by grouping.	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.