Comparer des méthodes

	Recherche hiérarchique confirmatoire	Modélisation par équations structurelles
Domaine	Conception de la recherche	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1980s–2000s	1921
Auteur d'origine	Raudenbush & Bryk; Hox; Goldstein	Sewall Wright
Type	Quantitative confirmatory research design	Method
Source fondatrice	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.
Alias	multilevel confirmatory research, nested confirmatory design, hierarchical hypothesis-testing research, HCR	SEM, path analysis, latent variable modeling, causal modeling
Apparentées	5	3
Résumé	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.