Heterogeneity, school-effects and the North/South achievement gap in Italian secondary education: evidence from a three-level mixed model

Studying the relationship between anxiety and school achievement: evidence from PISA data

Using the Programme for International Student Assessment (PISA) 2015 data for Italy, this paper offers a complete overview of the relationship between test anxiety and school performance by studying how anxiety affects the performance of students along the overall conditional distribution of mathematics, literature and science scores. We aim to indirectly measure whether higher goals increase test anxiety, starting from the hypothesis that high-skilled students generally set themselves high goals. We use an M-quantile regression approach that allows us to take into account the hierarchical structure and sampling weights of the PISA data. There is evidence of a negative and statistically significant relationship between test anxiety and school performance. The size of the estimated association is greater at the upper tail of the distribution of each score than at the lower tail. Therefore, our results suggest that high-performing students are more affected than low-performing students by emotional reactions to tests and school-work anxiety.

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

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.