Does class matter more than school? Evidence from a multilevel statistical analysis on Italian junior secondary school students

Abstract

This paper assesses the differences in educational attainments between students across classes and schools they are grouped by, in the context of Italian educational system. The purpose is to identify a relationship between pupils' reading test scores and students' characteristics, stratifying for classes, schools and geographical areas. The dataset contains detailed information about more than 500,000 students at the first year of junior secondary school in the year 2012/2013. By means of multilevel linear models, it is possible to estimate statistically significant school and class effects, after adjusting for pupil's characteristics, including prior achievement. The results show that school and class effects are very heterogeneous across macro-areas (Northern, Central and Southern Italy), and that there are substantial discrepancies between and within schools; overall, class effects on achievement tend to be larger than school ones.

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

“…Therefore, INVALSI own statistical analyses reveal that there are not striking differences in the results that can be obtained when considering alternatively these two subjects. Applications of similar models to the set of reading scores instead of mathematics seem to confirm the main phenomena that are described in the present research (see preliminary results in Masci et al 2016).…”

“…, J (R) ). While in theory adding class-level variables can be interesting to understand the key determinants of students achievement at that level of analysis, this is not the primary scope of the paper (the interested reader can look at Masci et al 2016, where we focus on this topic). The estimation of class effects, in the present work, should be interpreted as a byproduct of the main analysis, with the sole interest on showing the relative weight of variance at student, class and school levels.…”

“…among others, Goldstein et al 1993;Goldstein and Sammons 1997 and references therein). Few papers still exist about the Italian case (notable exceptions are Agasisti and Vittadini 2012;Bratti et al 2007;Masci et al 2016).…”

Semiparametric Mixed Effects Models for Unsupervised Classification of Italian Schools

“…In this section, we present the semiparametric mixed effects model (Section 2.1), the EM algorithm for the estimation of its parameters (Section 2.2) and a simulation study (Section 2.3). Since we know from previous research on Italian data that there are patterns of student achievements across different Italian schools (Agasisti et al ., ; Masci et al ., , ), we are interested in evaluating how the association between previous and current student test scores changes across different Italian schools and, in particular, in identifying subpopulations of schools within which this association is identical. Therefore, the model that we develop is a two‐level linear model (in the application, students represent level 1 and schools represent level 2) with a discrete distribution with a finite number of support points on the random effects.…”

“…() and Masci et al . (, ) observed that the percentage of variability in student attainments in INVALSI tests explained by the random effect depends on the geographical macroarea and differs between mathematics and reading performances. In particular, this percentage is higher in mathematics and especially in southern Italy, reaching peaks of 20%.…”