Large Arizona school district dataset for EL reclassification modeling¶
Product or Programme · All products and programmes
Evidence · 2 claims (2 for) · 1 study (1 associational),q2· 0 of 1 report an effect size · 2 claims rest on one study
Description¶
A large Arizona school district longitudinal dataset used by Tyler Matta to jointly model English-proficiency development and the timing of English-learner reclassification.
Components¶
Claims¶
- The SREM predicts time to reclassification more accurately than a conventional discrete-time hazard model [+W]
- Information about English-language development is critical for accurately predicting the grade an EL will reclassify [+W]
Related Products and Programmes¶
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Key Sources¶
- Tyler Matta, James Soland. (2018). Predicting time to reclassification for English learners: A joint modeling approach. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/1076998618791259
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
large-arizona-school-district-dataset-for-el-reclassification-modeling |
title |
Large Arizona school district dataset for EL reclassification modeling |
description |
A large Arizona school district longitudinal dataset used by Tyler Matta to jointly model English-proficiency development and the timing of English-learner reclassification. |
status |
review |
moved_from |
element/arizona-district-el-reclassification-dataset |
product_kind |
dataset |
generated |
by process:sweep-kinds on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — tyler-matta-2018 |
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