The Effect of M@tabel on Italian Students’ Performances: A Quantile Regression Approach

Abstract

The effectiveness of a training program on students' achievements has primarily relied on estimation approaches which capture the mean effect on students' performances. While estimating how "on average" variables affect educational outcomes yields straightforward interpretations, the standard methodology may miss what is crucial for policy purposes, namely how educational programs affect students achievements differently at different points of the conditional test score distribution. The aim of this study is to investigate the short term effects of M@tabel on Italian sixth grade students performance in mathematics at secondary schools through a multilevel quantile regression approach. The proposed model allows to fully characterize the entire conditional distribution of performances in mathematics, providing a more complete view of a possible relationship between M@tabel treatment and the observed math score gain. The main concern is not only "does the training program have an impact" but also the question "for whom does it matter, and how".

The Use of Sampling Weights inM-Quantile Random-Effects Regression: An Application to Programme for International Student Assessment Mathematics Scores

“…In the context of educational research, there are not many examples of studies that applied multilevel quantile regression models. Costanzo (2015) used the LQMM to evaluate the effect of a specific training programme (M@tabel) on the Italian sixth grade students' performance in mathematics at secondary schools: the author highlighted the advantages of using this model compared to the traditional linear random effects one. Faria and Portela (2016) applied LQMM to analyse the determinants of students' success in Portugal using 2009 PISA survey.…”