So sánh phương pháp

	Educational Hierarchical Linear Modeling	Mô hình Tuyến tính Phân cấp (HLM)
Lĩnh vực	Education	Thống kê
Họ	Regression model	Regression model
Năm ra đời	2002	1992
Người khởi xướng	Stephen Raudenbush & Anthony Bryk	Bryk & Raudenbush
Loại	Multilevel regression for hierarchically nested educational data	Multilevel linear regression
Công trình gốc	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
Tên gọi khác	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
Liên quan	4	4
Tóm tắt	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.