Publication Results

Code: 24/2019
Title: Evaluating class and school effects on the joint achievements in different subjects: a bivariate semi-parametric mixed-effects model
Date: Friday 5th July 2019
Author(s) : Masci, C.; Ieva, F.; Agasisti, T.; Paganoni A.m.
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Abstract: This paper proposes an innovative statistical method to measure the impact of the class/school on its student achievements in multiple subjects. We propose a semi-parametric mixed-effects model with a bivariate response variable, where the random effects are assumed to follow a discrete distribution with an unknown number of support points, together with an Expectation-Maximization algorithm to estimate its parameters. The bivariate setting allows to estimate the distributions of the model coefficients related to each response variable as well as their joint distribution. 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 two different subjects (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.

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Published: Masci, C., Ieva, F., Agasisti, T., & Paganoni, A. M. (2021). Evaluating class and school effects on the joint student achievements in different subjects: a bivariate semiparametric model with random coefficients. Computational Statistics, 1-41. DOI: