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SREM

Research Method · All research methods
Evidence · 2 claims (2 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 2 claims rest on one study

Description

The article develops a shared random effects model (SREM) to estimate English proficiency development and time to reclassification simultaneously. It works by treating student-specific random effects as latent covariates in the time to reclassification model, so that growth in academic English proficiency informs predictions of when an English learner will reclassify to fluent English proficient status. The model was applied to data from a large Arizona school district.

Accounts

  • Shared random effects model jointly estimating English proficiency development and time to reclassification: The article develops a shared random effects model (SREM) to estimate English proficiency development and time to reclassification simultaneously. It works by treating student-specific random effects as latent covariates in the time to reclassification model, so that growth in academic English proficiency informs predictions of when an English learner will reclassify to fluent English proficient status. The model was applied to data from a large Arizona school district. (Tyler Matta (2018))

Claims

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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 research-method
id srem
title SREM
description The article develops a shared random effects model (SREM) to estimate English proficiency development and time to reclassification simultaneously.
status review
moved_from theory/srem-joint-english-proficiency-reclassification
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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