Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| School Effectiveness Modeling× | Educational Hierarchical Linear Modeling× | |
|---|---|---|
| Domeniu | Education | Education |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 2000 | 2002 |
| Autorul original≠ | School effectiveness research tradition (Edmonds; Rutter; Teddlie & Reynolds; multilevel methods of Aitkin & Longford) | Stephen Raudenbush & Anthony Bryk |
| Tip≠ | Multilevel modeling of school contributions to student outcomes net of intake | Multilevel regression for hierarchically nested educational data |
| Sursa seminală≠ | Teddlie, C., & Reynolds, D. (2000). The International Handbook of School Effectiveness Research. Falmer Press. ISBN: 9780750706070 | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 9780761919049 |
| Denumiri alternative | School Effects Research, Educational Effectiveness Modeling, School Performance Modeling, Differential School Effectiveness | Multilevel Models in Education, Students-in-Schools HLM, School Effects Multilevel Model, Random-Effects Models for Educational Data |
| Înrudite | 4 | 4 |
| Rezumat≠ | 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. | 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. |
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