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Bivariate multilevel models for the analysis of mathematics and reading pupils' achievements

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  • C. Masci
  • F. Ieva
  • T. Agasisti
  • A. M. Paganoni

Abstract

The purpose of this paper is to identify a relationship between pupils' mathematics and reading test scores and the characteristics of students themselves, stratifying for classes, schools and geographical areas. The data set of interest contains detailed information about more than 500,000 students at the first year of junior secondary school in the year 2012/2013, provided by the Italian Institute for the Evaluation of Educational System. The innovation of this work is in the use of multivariate multilevel models, in which the outcome is bivariate: reading and mathematics achievement. Using the bivariate outcome enables researchers to analyze the correlations between achievement levels in the two fields and to predict statistically significant school and class effects after adjusting for pupil's characteristics. The statistical model employed here explicates account for the potential covariance between the two topics, and at the same time it allows the school effect to vary among them. The results show that while for most cases the direction of school's effect is coherent for reading and mathematics (i.e. positive/negative), there are cases where internal school factors lead to different performances in the two fields.

Suggested Citation

  • C. Masci & F. Ieva & T. Agasisti & A. M. Paganoni, 2017. "Bivariate multilevel models for the analysis of mathematics and reading pupils' achievements," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(7), pages 1296-1317, May.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:7:p:1296-1317
    DOI: 10.1080/02664763.2016.1201799
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    Cited by:

    1. 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.
    2. Masci, Chiara & Johnes, Geraint & Agasisti, Tommaso, 2018. "Student and school performance across countries: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1072-1085.
    3. 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.
    4. Camanho, Ana S. & Varriale, Luisa & Barbosa, Flávia & Sobral, Thiago, 2021. "Performance assessment of upper secondary schools in Italian regions using a circular pseudo-Malmquist index," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1188-1208.
    5. Francesco Schirripa Spagnolo & Nicola Salvati & Antonella D’Agostino & Ides Nicaise, 2020. "The use of sampling weights in M‐quantile random‐effects regression: an application to Programme for International Student Assessment mathematics scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 991-1012, August.

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