방법 비교

	Cross-Classified Multilevel Models in Education	부가가치 모델링
분야	Education	심리측정학
계열	Regression model	Latent structure
기원 연도	1993	1998
창시자	Multilevel modeling community (Raudenbush; Goldstein; Rasbash & Browne)	William Sanders, Sandra Horn
유형	Multilevel model with units cross-classified by two or more non-nested groupings	Longitudinal student achievement modeling
원전	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.
별칭	Cross-Classified Random Effects Models, CCREM, Cross-Classified Multilevel Modeling, Multiple Membership Cross-Classified Models	VAM
관련	4	4
요약	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.