Comparar métodos

	Educational Hierarchical Linear Modeling	Modelado Lineal Jerárquico (HLM / Modelado Multinivel)
Campo	Education	Estadística
Familia	Regression model	Hypothesis test
Año de origen	2002	1986
Autor original	Stephen Raudenbush & Anthony Bryk	Raudenbush & Bryk (popularized); Goldstein (parallel development)
Tipo	Multilevel regression for hierarchically nested educational data	Parametric nested-data regression
Fuente seminal	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 9780761919049	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049
Alias	Multilevel Models in Education, Students-in-Schools HLM, School Effects Multilevel Model, Random-Effects Models for Educational Data	HLM, MLM, multilevel modeling, multilevel analysis
Relacionados	4	4
Resumen	Educational hierarchical linear modeling (HLM) is a multilevel regression framework for data in which students are nested within classrooms and classrooms within schools. Formalized for education by Raudenbush and Bryk, it lets the intercept and slopes of a student-level regression vary across schools, simultaneously estimating student-level relationships, school-level relationships, and the cross-level interactions between them — while producing correct standard errors that single-level regression on clustered data cannot.	Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels.