Comparer des méthodes

	Recherche causale-comparative hiérarchique	Modélisation multiniveau
Domaine	Conception de la recherche	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1960s (causal-comparative); 1980s–2002 (hierarchical/multilevel extension)	1992
Auteur d'origine	Kerlinger (causal-comparative logic); Raudenbush & Bryk (hierarchical extension)	Anthony Bryk and Stephen Raudenbush
Type	Non-experimental quantitative 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	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Alias	multilevel causal-comparative design, nested causal-comparative research, HLM causal-comparative study, hierarchical ex post facto comparison	HLM, mixed-effects models, random effects models, MLM
Apparentées	4	3
Résumé	Hierarchical causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on an outcome variable while explicitly modeling the nested structure of the data. Participants are clustered within higher-level units — students within classrooms, employees within organizations — and the design uses multilevel analytical techniques to distinguish group differences at each level. The cause-and-effect inference is strengthened by accounting for variance attributable to the hierarchy rather than misattributing it to individual-level group membership.	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.