방법 비교

	School Effectiveness Modeling	Educational Production Function
분야	Education	Education
계열	Regression model	Regression model
기원 연도	2000	1979
창시자	School effectiveness research tradition (Edmonds; Rutter; Teddlie & Reynolds; multilevel methods of Aitkin & Longford)	Economics of education (Coleman; Hanushek; Todd & Wolpin)
유형	Multilevel modeling of school contributions to student outcomes net of intake	Regression relating educational inputs to achievement outputs
원전	Teddlie, C., & Reynolds, D. (2000). The International Handbook of School Effectiveness Research. Falmer Press. ISBN: 9780750706070	Hanushek, E. A. (1979). Conceptual and empirical issues in the estimation of educational production functions. Journal of Human Resources, 14(3), 351–388.
별칭	School Effects Research, Educational Effectiveness Modeling, School Performance Modeling, Differential School Effectiveness	Education Production Function, Schooling Production Function, Input-Output Model of Education, Achievement Production Function
관련	4	3
요약	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.	The educational production function is the economist's framework for relating the inputs of schooling — class size, teacher quality, expenditure, family background — to an output, usually measured achievement. Borrowing the production-function metaphor from the economics of the firm, it estimates by how much achievement changes when an input changes. It is the analytic backbone of decades of debate over what resources matter for learning, and the methodological challenges of estimating it honestly — endogeneity, omitted variables, and the cumulative history of inputs — define much of the field.