IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v44y2019i1p78-102.html

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998618791259
    Download Restriction: no
    File URL: https://libkey.io/10.3102/1076998618791259?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    2. Lewandowski, Daniel & Kurowicka, Dorota & Joe, Harry, 2009. "Generating random correlation matrices based on vines and extended onion method," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1989-2001, October.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Yixiao & Yu, Cindy L. & Li, Haitao, 2022. "Nowcasting GDP Using Dynamic Factor Model with Unknown Number of Factors and Stochastic Volatility: A Bayesian Approach," Econometrics and Statistics, Elsevier, vol. 24(C), pages 75-93.
    2. Matthias Breuer & Harm H. Schütt, 2023. "Accounting for uncertainty: an application of Bayesian methods to accruals models," Review of Accounting Studies, Springer, vol. 28(2), pages 726-768, June.
    3. Liya Fu & Zhuoran Yang & Yan Zhou & You-Gan Wang, 2021. "An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 679-709, October.
    4. Zhang, Zili & Charalambous, Christiana & Foster, Peter, 2023. "A Gaussian copula joint model for longitudinal and time-to-event data with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    5. Horrocks, Julie & Rueffer, Matthew, 2014. "A Bayesian approach to estimating animal density from binary acoustic transects," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 17-25.
    6. JD Opdyke, 2025. "Beyond Correlation: Positive Definite Dependence Measures for Robust Inference, Flexible Scenarios, and Causal Modeling for Financial Portfolios," Papers 2504.15268, arXiv.org, revised Jan 2026.
    7. Flórez, Alvaro J. & Molenberghs, Geert & Van der Elst, Wim & Alonso Abad, Ariel, 2022. "An efficient algorithm to assess multivariate surrogate endpoints in a causal inference framework," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    8. Schweitzer, Maurice E. & Hershey, John C. & Bradlow, Eric T., 2006. "Promises and lies: Restoring violated trust," Organizational Behavior and Human Decision Processes, Elsevier, vol. 101(1), pages 1-19, September.
    9. Giuseppe Brandi & Ruggero Gramatica & Tiziana Di Matteo, 2019. "Unveil stock correlation via a new tensor-based decomposition method," Papers 1911.06126, arXiv.org, revised Apr 2020.
    10. Andrew Y. Chen & Jack McCoy, 2022. "Missing Values Handling for Machine Learning Portfolios," Papers 2207.13071, arXiv.org, revised Jan 2024.
    11. Lin, Zhengzhi & Liu, Xiao & Xiang, Yisha & Hong, Yili, 2025. "Modeling multivariate degradation data with dynamic covariates under a Bayesian framework," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    12. Gregory Benton & Wesley J. Maddox & Andrew Gordon Wilson, 2022. "Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes," Papers 2207.06544, arXiv.org.
    13. Juho Kettunen & Lauri Mehtätalo & Eeva‐Stiina Tuittila & Aino Korrensalo & Jarno Vanhatalo, 2024. "Joint species distribution modeling with competition for space," Environmetrics, John Wiley & Sons, Ltd., vol. 35(2), March.
    14. Pedro Luis do N. Silva & Fernando Antônio da S. Moura, 2022. "Fitting multivariate multilevel models under informative sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1663-1678, October.
    15. Melody Huang & Cory McCartan, 2025. "Relative Bias Under Imperfect Identification in Observational Causal Inference," Papers 2507.23743, arXiv.org, revised Mar 2026.
    16. Hirofumi Michimae & Takeshi Emura, 2022. "Bayesian ridge estimators based on copula-based joint prior distributions for regression coefficients," Computational Statistics, Springer, vol. 37(5), pages 2741-2769, November.
    17. repec:plo:pone00:0198620 is not listed on IDEAS
    18. Aleksandra Gorzycka-Sikora & Nancy Mock & Michelle Lacey, 2023. "Multivariate analysis of food consumption profiles in crisis settings," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-22, March.
    19. Madar, Vered, 2015. "Direct formulation to Cholesky decomposition of a general nonsingular correlation matrix," Statistics & Probability Letters, Elsevier, vol. 103(C), pages 142-147.
    20. Rizopoulos, Dimitris, 2016. "The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data Using MCMC," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i07).
    21. Rui Martins, 2022. "A flexible link for joint modelling longitudinal and survival data accounting for individual longitudinal heterogeneity," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 41-61, March.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:jedbes:v:44:y:2019:i:1:p:78-102. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.