Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Cross-Classified Multilevel Models in Education× | Modelēšana, balstoties uz pievienoto vērtību× | |
|---|---|---|
| Nozare≠ | Education | Psihometrija |
| Saime≠ | Regression model | Latent structure |
| Izcelsmes gads≠ | 1993 | 1998 |
| Autors≠ | Multilevel modeling community (Raudenbush; Goldstein; Rasbash & Browne) | William Sanders, Sandra Horn |
| Tips≠ | Multilevel model with units cross-classified by two or more non-nested groupings | Longitudinal student achievement modeling |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi≠ | Cross-Classified Random Effects Models, CCREM, Cross-Classified Multilevel Modeling, Multiple Membership Cross-Classified Models | VAM |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | 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. |
| ScholarGateDatu kopa ↗ |
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