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Predicting Time to Reclassification for English Learners: A Joint Modeling Approach

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  • Tyler H. Matta

    (Pearson
    University of Oslo)

  • James Soland

    (NWEA)

Abstract

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.

Suggested Citation

  • Tyler H. Matta & James Soland, 2019. "Predicting Time to Reclassification for English Learners: A Joint Modeling Approach," Journal of Educational and Behavioral Statistics, , vol. 44(1), pages 78-102, February.
    • Handle: RePEc:sae:jedbes:v:44:y:2019:i:1:p:78-102
    DOI: 10.3102/1076998618791259
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    References listed on IDEAS

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    1. Joseph P. Robinson‐Cimpian & Karen D. Thompson, 2016. "The Effects of Changing Test‐Based Policies for Reclassifying English Learners," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 35(2), pages 279-305, April.
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    3. Betsy J. Feldman & Sophia Rabe-Hesketh, 2012. "Modeling Achievement Trajectories When Attrition Is Informative," Journal of Educational and Behavioral Statistics, , vol. 37(6), pages 703-736, December.
    4. Guo X. & Carlin B.P., 2004. "Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages," The American Statistician, American Statistical Association, vol. 58, pages 16-24, February.
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