Methoden vergleichen

	Hierarchische Modelltestforschung	Multilevel Modeling
Fachgebiet	Forschungsdesign	Forschungsstatistik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994)	1992
Urheber	Stephen Raudenbush and Anthony Bryk (HLM); extended to multilevel SEM by Bengt Muthen	Anthony Bryk and Stephen Raudenbush
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	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Aliasnamen	multilevel model testing, hierarchical SEM, nested model testing, HLM model testing	HLM, mixed-effects models, random effects models, MLM
Verwandt	5	3
Zusammenfassung	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.	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.