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Variable selection in latent variable models via knockoffs: an application to international large-scale assessment in education

Author

Listed:
  • Xie, Zilong
  • Chen, Yunxiao
  • von Davier, Matthias
  • Weng, Haolei

Abstract

International large-scale assessments (ILSAs) play an important role in educational research and policy making. They collect valuable data on education quality and performance development across many education systems, giving countries the opportunity to share techniques, organisational structures, and policies that have proven efficient and successful. To gain insights from ILSA data, we identify non-cognitive variables associated with students’ academic performance. This problem has three analytical challenges: (a) academic performance is measured by cognitive items under a matrix sampling design; (b) there are many missing values in the non-cognitive variables; and (c) multiple comparisons due to a large number of non-cognitive variables. We consider an application to the Programme for International Student Assessment, aiming to identify non-cognitive variables associated with students’ performance in science. We formulate it as a variable selection problem under a general latent variable model framework and further propose a knockoff method that conducts variable selection with a controlled error rate for false selections.

Suggested Citation

  • Xie, Zilong & Chen, Yunxiao & von Davier, Matthias & Weng, Haolei, 2023. "Variable selection in latent variable models via knockoffs: an application to international large-scale assessment in education," LSE Research Online Documents on Economics 120812, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:120812
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    References listed on IDEAS

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    1. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2021. "On the Treatment of Missing Data in Background Questionnaires in Educational Large-Scale Assessments: An Evaluation of Different Procedures," Journal of Educational and Behavioral Statistics, , vol. 46(4), pages 430-465, August.
    2. Jianqing Fan & Han Liu & Yang Ning & Hui Zou, 2017. "High dimensional semiparametric latent graphical model for mixed data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 405-421, March.
    3. Jingchen Liu & Andrew Gelman & Jennifer Hill & Yu-Sung Su & Jonathan Kropko, 2014. "On the stationary distribution of iterative imputations," Biometrika, Biometrika Trust, vol. 101(1), pages 155-173.
    4. Yingying Fan & Jinchi Lv & Mahrad Sharifvaghefi & Yoshimasa Uematsu, 2020. "IPAD: Stable Interpretable Forecasting with Knockoffs Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1822-1834, December.
    5. Emmanuel Candès & Yingying Fan & Lucas Janson & Jinchi Lv, 2018. "Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(3), pages 551-577, June.
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    More about this item

    Keywords

    Model-X knockoffs; missing data; latent variables; variable selection; international large-scale assessment;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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