Comparer des méthodes

	Recherche hiérarchique confirmatoire	Recherche sur les tests de modèles hiérarchiques
Domaine	Conception de la recherche	Conception de la recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1980s–2000s	1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994)
Auteur d'origine	Raudenbush & Bryk; Hox; Goldstein	Stephen Raudenbush and Anthony Bryk (HLM); extended to multilevel SEM by Bengt Muthen
Type	Quantitative confirmatory research design	Quantitative confirmatory research design
Source fondatrice	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049
Alias	multilevel confirmatory research, nested confirmatory design, hierarchical hypothesis-testing research, HCR	multilevel model testing, hierarchical SEM, nested model testing, HLM model testing
Apparentées	5	5
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.	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.