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Semiparametric mixed effects models for unsupervised classification of Italian schools

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  • Chiara Masci
  • Anna Maria Paganoni
  • Francesca Ieva

Abstract

The main purpose of the paper is to improve research on school effectiveness by applying a new strategy for uncovering subpopulations of schools that differ in terms of distribution of student outcomes. We propose a semiparametric mixed effects model with an expectation–maximization algorithm to estimate its parameters and we apply it to the Italian Institute for the Educational Evaluation of Instruction and Training data of 2013–2014 as a tool for the identification of latent subpopulations of schools. The semiparametric assumption provides the random effects of the mixed effects model to be distributed according to a discrete distribution with an (a priori) unknown number of support points. This modelling induces an automatic clustering of schools (the higher level of hierarchy), where schools within the same cluster share the same random effects. The latent subpopulations of schools identified may then be exploited through the use of multinomial models that include school level features. The novelties introduced by this paper are twofold: first, the semiparametric expectation–maximization algorithm is an innovative method that could be used in many classification problems; second, its application to education data represents a new approach to study school effectiveness.

Suggested Citation

  • Chiara Masci & Anna Maria Paganoni & Francesca Ieva, 2019. "Semiparametric mixed effects models for unsupervised classification of Italian schools," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1313-1342, October.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:4:p:1313-1342
    DOI: 10.1111/rssa.12449
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    Cited by:

    1. G. De Novellis & M. Doretti & G. E. Montanari & M. G. Ranalli & N. Salvati, 2024. "Performance evaluation of nursing homes using finite mixtures of logistic models and M-quantile regression for binary data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(3), pages 753-781, July.
    2. 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.
    3. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.

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