Yöntem Karşılaştırma

	Cross-Classified Multilevel Models in Education	Katma Değer Modellemesi
Alan	Education	Psikometri
Aile	Regression model	Latent structure
Köken yılı	1993	1998
Köken	Multilevel modeling community (Raudenbush; Goldstein; Rasbash & Browne)	William Sanders, Sandra Horn
Tür	Multilevel model with units cross-classified by two or more non-nested groupings	Longitudinal student achievement modeling
Seminal kaynak	Goldstein, H. (2011). Multilevel Statistical Models (4th ed.). Wiley. ISBN: 9780470748657	Kane, T. J., Rockoff, J. E., & Staiger, D. O. (2008). What does certification tell us about teacher effectiveness? Evidence from New York City. Economics of Education Review, 27(6), 615-631.
Diğer adlar	Cross-Classified Random Effects Models, CCREM, Cross-Classified Multilevel Modeling, Multiple Membership Cross-Classified Models	VAM
İlişkili	4	4
Özet	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.	Value-Added Modeling (VAM) is a method for assessing the contribution of schools or teachers to student achievement growth, developed by Sanders and Horn (1998). VAM isolates the effect of a teacher or school by comparing student gains (value added) while controlling for prior achievement and student characteristics.