Σύγκριση μεθόδων

	Cross-Classified Multilevel Models in Education	Educational Hierarchical Linear Modeling
Πεδίο	Education	Education
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1993	2002
Δημιουργός	Multilevel modeling community (Raudenbush; Goldstein; Rasbash & Browne)	Stephen Raudenbush & Anthony Bryk
Τύπος	Multilevel model with units cross-classified by two or more non-nested groupings	Multilevel regression for hierarchically nested educational data
Θεμελιώδης πηγή	Goldstein, H. (2011). Multilevel Statistical Models (4th ed.). Wiley. ISBN: 9780470748657	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 9780761919049
Εναλλακτικές ονομασίες	Cross-Classified Random Effects Models, CCREM, Cross-Classified Multilevel Modeling, Multiple Membership Cross-Classified Models	Multilevel Models in Education, Students-in-Schools HLM, School Effects Multilevel Model, Random-Effects Models for Educational Data
Συναφείς	4	4
Σύνοψη	Cross-classified multilevel models extend hierarchical linear modeling to situations where units belong to two or more groupings that do not nest neatly inside one another. In education, students are often classified by both school and neighborhood, or by primary and secondary school across time — classifications that cut across each other rather than form a clean hierarchy. These models assign a random effect to each classification simultaneously, partitioning variance among them and yielding correct inferences where a purely nested model would be misspecified.	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.