Methoden vergleichen

	Hierarchical Linear Model (HLM)	Multilevel Modeling
Fachgebiet	Statistik	Forschungsstatistik
Familie	Regression model	Process / pipeline
Entstehungsjahr	1992	1992
Urheber	Bryk & Raudenbush	Anthony Bryk and Stephen Raudenbush
Typ	Multilevel linear regression	Method
Wegweisende Quelle	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Aliasnamen	HLM, multilevel linear model, nested data model, random coefficient model	HLM, mixed-effects models, random effects models, MLM
Verwandt	4	3
Zusammenfassung	The Hierarchical Linear Model (HLM) is a multilevel regression method designed for data in which lower-level units (e.g., students, patients) are nested within higher-level groups (e.g., schools, hospitals). It simultaneously models within-group relationships and between-group variation, producing unbiased estimates and correct standard errors that ordinary regression cannot provide for nested data.	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.