Vertaile menetelmiä

	School Effectiveness Modeling	Cross-Classified Multilevel Models in Education
Tieteenala	Education	Education
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2000	1993
Kehittäjä	School effectiveness research tradition (Edmonds; Rutter; Teddlie & Reynolds; multilevel methods of Aitkin & Longford)	Multilevel modeling community (Raudenbush; Goldstein; Rasbash & Browne)
Tyyppi	Multilevel modeling of school contributions to student outcomes net of intake	Multilevel model with units cross-classified by two or more non-nested groupings
Alkuperäislähde	Teddlie, C., & Reynolds, D. (2000). The International Handbook of School Effectiveness Research. Falmer Press. ISBN: 9780750706070	Goldstein, H. (2011). Multilevel Statistical Models (4th ed.). Wiley. ISBN: 9780470748657
Rinnakkaisnimet	School Effects Research, Educational Effectiveness Modeling, School Performance Modeling, Differential School Effectiveness	Cross-Classified Random Effects Models, CCREM, Cross-Classified Multilevel Modeling, Multiple Membership Cross-Classified Models
Liittyvät	4	4
Tiivistelmä	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.	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.