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Evaluating class and school effects on the joint student achievements in different subjects: a bivariate semiparametric model with random coefficients

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Listed:
  • Chiara Masci

    (Politecnico di Milano)

  • Francesca Ieva

    (Politecnico di Milano)

  • Tommaso Agasisti

    (Politecnico di Milano)

  • Anna Maria Paganoni

    (Politecnico di Milano)

Abstract

This paper proposes an innovative statistical method to measure the impact of the class/school on student achievements in multiple subjects. We propose a semiparametric model for a bivariate response variable with random coefficients, that are assumed to follow a discrete distribution with an unknown number of support points, together with an Expectation-Maximization algorithm—called BSPEM algorithm—to estimate its parameters. In the case study, we apply the BSPEM algorithm to data about Italian middle schools, considering students nested within classes, and we identify subpopulations of classes, standing on their effects on student achievements in reading and mathematics. The proposed model is extremely informative in exploring the correlation between multiple class effects, which are typical of the educational production function. The estimated class effects on reading and mathematics student achievements are then explained in terms of various class and school level characteristics selected by means of a LASSO regression.

Suggested Citation

  • Chiara Masci & Francesca Ieva & Tommaso Agasisti & Anna Maria Paganoni, 2021. "Evaluating class and school effects on the joint student achievements in different subjects: a bivariate semiparametric model with random coefficients," Computational Statistics, Springer, vol. 36(4), pages 2337-2377, December.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:4:d:10.1007_s00180-021-01107-1
    DOI: 10.1007/s00180-021-01107-1
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