Methoden vergleichen

	Hierarchische konfirmatorische Forschung	Strukturelle Gleichungsmodellierung
Fachgebiet	Forschungsdesign	Forschungsstatistik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1980s–2000s	1921
Urheber	Raudenbush & Bryk; Hox; Goldstein	Sewall Wright
Typ	Quantitative confirmatory research design	Method
Wegweisende Quelle	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.
Aliasnamen	multilevel confirmatory research, nested confirmatory design, hierarchical hypothesis-testing research, HCR	SEM, path analysis, latent variable modeling, causal modeling
Verwandt	5	3
Zusammenfassung	Hierarchical confirmatory research is a quantitative design that tests pre-specified hypotheses about relationships or group differences in data that have a natural nested (hierarchical) structure — such as students clustered within classrooms, patients within hospitals, or employees within organizations. By explicitly modeling the hierarchy, it avoids the inflation of Type I error that occurs when nested data are analyzed as though observations were independent.	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.