%0 Journal Article
%T Predicting Time to Reclassification for English Learners: A Joint Modeling Approach
%A James Soland
%A Tyler H. Matta
%J Journal of Educational and Behavioral Statistics
%@ 1935-1054
%D 2019
%R 10.3102/1076998618791259
%X The development of academic English proficiency and the time it takes to reclassify to fluent English proficient status are key issues in English learner (EL) policy. This article develops a shared random effects model (SREM) to estimate English proficiency development and time to reclassification simultaneously, treating student-specific random effects as latent covariates in the time to reclassification model. Using data from a large Arizona school district, the SREM resulted in predictions of time to reclassification that were 93% accurate compared to 85% accuracy from a conventional discrete-time hazard model used in prior literature. The findings suggest that information about English-language development is critical for accurately predicting the grade an EL will reclassify
%K joint modeling
%K longitudinal data
%K classification accuracy
%U https://journals.sagepub.com/doi/full/10.3102/1076998618791259