เปรียบเทียบวิธี

	School Effectiveness Modeling	Educational Hierarchical Linear Modeling
สาขาวิชา	Education	Education
ตระกูล	Regression model	Regression model
ปีกำเนิด	2000	2002
ผู้ริเริ่ม	School effectiveness research tradition (Edmonds; Rutter; Teddlie & Reynolds; multilevel methods of Aitkin & Longford)	Stephen Raudenbush & Anthony Bryk
ประเภท	Multilevel modeling of school contributions to student outcomes net of intake	Multilevel regression for hierarchically nested educational data
แหล่งต้นตำรับ	Teddlie, C., & Reynolds, D. (2000). The International Handbook of School Effectiveness Research. Falmer Press. ISBN: 9780750706070	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 9780761919049
ชื่อเรียกอื่น	School Effects Research, Educational Effectiveness Modeling, School Performance Modeling, Differential School Effectiveness	Multilevel Models in Education, Students-in-Schools HLM, School Effects Multilevel Model, Random-Effects Models for Educational Data
ที่เกี่ยวข้อง	4	4
สรุป	School effectiveness modeling estimates how much, and in what ways, individual schools contribute to student outcomes once differences in what students bring with them are taken into account. Using multilevel (hierarchical) models, it adjusts for student intake — prior attainment, socioeconomic background — and isolates the residual variation attributable to schools. The field asks not just whether schools differ, but which factors make some schools more effective and for whom, distinguishing genuine school contributions from the composition of their intake.	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.