Comparer des méthodes

	Recherche hiérarchique confirmatoire	Recherche confirmatrice
Domaine	Conception de la recherche	Conception de la recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1980s–2000s	1934 (Popper); widely adopted in social sciences from 1960s onward
Auteur d'origine	Raudenbush & Bryk; Hox; Goldstein	Karl Popper (falsificationism); formalized in behavioral sciences by Paul Meehl and others
Type	Quantitative confirmatory research design	Quantitative 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	Popper, K. R. (1959). The Logic of Scientific Discovery. Hutchinson. ISBN: 978-0415278447
Alias	multilevel confirmatory research, nested confirmatory design, hierarchical hypothesis-testing research, HCR	hypothesis-testing research, deductive research, theory-testing research, confirmatory study
Apparentées	5	4
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.	Confirmatory research is a deductive quantitative design in which the researcher specifies hypotheses derived from existing theory before data collection, then tests whether the data support or refute those hypotheses. Unlike exploratory approaches that generate ideas from data, confirmatory research begins with an established theoretical framework, pre-registers predictions, and applies statistical tests to evaluate those predictions against empirical evidence. It is the backbone of hypothesis-driven social, behavioral, and health science inquiry.