방법 비교

	Value-Added Teacher Evaluation	Educational Hierarchical Linear Modeling
분야	Education	Education
계열	Regression model	Regression model
기원 연도	2004	2002
창시자	William Sanders (TVAAS); methodological critique by McCaffrey, Lockwood, Koretz et al.	Stephen Raudenbush & Anthony Bryk
유형	Statistical estimation of individual teachers' contributions to student achievement growth	Multilevel regression for hierarchically nested educational data
원전	McCaffrey, D. F., Lockwood, J. R., Koretz, D., Louis, T. A., & Hamilton, L. (2004). Models for value-added modeling of teacher effects. Journal of Educational and Behavioral Statistics, 29(1), 67–101.	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 9780761919049
별칭	Teacher Value-Added Models, VAM for Teachers, Teacher Effect Estimation, Value-Added Teacher Accountability	Multilevel Models in Education, Students-in-Schools HLM, School Effects Multilevel Model, Random-Effects Models for Educational Data
관련	4	4
요약	Value-added teacher evaluation uses longitudinal student test scores to estimate how much individual teachers contribute to their students' achievement growth, net of what students brought into the classroom. Statistically it applies value-added and mixed-model machinery — controlling for prior achievement and student characteristics, then treating each teacher's residual contribution as an effect to be estimated. Pioneered in Tennessee's TVAAS and scrutinized in a large methodological and policy literature, it became central, and controversial, in teacher accountability.	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.