Comparar métodos

	Educational Hierarchical Linear Modeling	Modelo Linear Hierárquico (HLM)
Área	Education	Estatística
Família	Regression model	Regression model
Ano de origem	2002	1992
Autor original	Stephen Raudenbush & Anthony Bryk	Bryk & Raudenbush
Tipo	Multilevel regression for hierarchically nested educational data	Multilevel linear regression
Fonte 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 Publications. ISBN: 978-0761919049
Outros nomes	Multilevel Models in Education, Students-in-Schools HLM, School Effects Multilevel Model, Random-Effects Models for Educational Data	HLM, multilevel linear model, nested data model, random coefficient model
Relacionados	4	4
Resumo	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.	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.