Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Cross-Classified Multilevel Models in Education× | Моделювання доданої вартості× | |
|---|---|---|
| Галузь≠ | Education | Психометрія |
| Родина≠ | Regression model | Latent structure |
| Рік появи≠ | 1993 | 1998 |
| Автор методу≠ | Multilevel modeling community (Raudenbush; Goldstein; Rasbash & Browne) | William Sanders, Sandra Horn |
| Тип≠ | Multilevel model with units cross-classified by two or more non-nested groupings | Longitudinal student achievement modeling |
| Основоположне джерело≠ | 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. DOI ↗ |
| Інші назви≠ | Cross-Classified Random Effects Models, CCREM, Cross-Classified Multilevel Modeling, Multiple Membership Cross-Classified Models | VAM |
| Пов'язані | 4 | 4 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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