Models comparing estimates of school effectiveness based on cross-sectional and longitudinal designs
Notice bibliographique
Résumé
The primary purpose of this study is to compare the six models (cross-sectional, two-wave, and multiwave, with and without controls) and determine which of the models most appropriately estimates school effects. For a fair and adequate evaluation of school effects, this study considers the following requirements of an appropriate analytical model. First, a model should have controls for students' background characteristics. Without controlling for the initial differences of students, one may not analyze the between-school differences appropriately, as students are not randomly assigned to schools. Second, a model should explicitly address individual change and growth rather than status, because students' learning and growth is the primary goal of schooling. In other words, studies should be longitudinal rather than cross-sectional. Most researches, however, have employed cross-sectional models because empirical methods of measuring change have been considered inappropriate and invalid. This study argues that the discussions about measuring change have been unjustifiably restricted to the two-wave model. It supports the idea of a more recent longitudinal approach to the measurement of change. That is, one can estimate the individual growth more accurately using multiwave data. Third, a model should accommodate the hierarchical characteristics of school data because schooling is a multilevel process. This study employs an Hierarchical Linear Model (HLM) as a basic methodological tool to analyze the data. The subjects of the study were 648 elementary students in 26 schools. The scores on three subtests of Canadian Tests of Basic Skills (CTBS) were collected for this grade cohort across three years (grades 5, 6 and 7). The between-school differences were analyzed using the six models previously mentioned. Students' general cognitive ability (CCAT) and gender were employed as the controls for background characteristics. Schools differed significantly in their average levels of academic achievement at grade 7 across the three subtests of CTBS. Schools also differed significantly in their average rates of growth in mathematics and reading between grades 5 and 7. One interesting finding was that the bias of the unadjusted model against adjusted model for the multiwave design was not as large as that for the cross-sectional design. Because the multiwave model deals with student growth explicitly and growth can be reliably estimated for some subject areas, even without controls for student intake, this study concluded that the multiwave models are a better design to estimate school effects. This study also discusses some practical implications and makes suggestions for further studies of school effects.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».