Exploiting TIMSS and PIRLS combined data: multivariate multilevel modelling of student achievement

TL;DR

This paper applies a multivariate multilevel model to analyze Italian fourth graders' achievement in reading, math, and science using TIMSS and PIRLS data, revealing how student and contextual factors influence performance.

Contribution

It introduces a multivariate multilevel modeling approach to jointly analyze multiple achievement outcomes and account for territorial differences in educational data.

Findings

Traditional factors influence achievement as expected

Territorial wealth impacts student performance

Identifies classes with notably high or low achievement

Abstract

We exploit a multivariate multilevel model for the analysis of the Italian sample of the TIMSS\&PIRLS 2011 Combined International Database on fourth grade students. The multivariate approach jointly considers educational achievement on Reading, Mathematics and Science, thus allowing us to test for differential associations of the covariates with the three outcomes, and to estimate the residual correlations between pairs of outcomes at student and class levels. Multilevel modelling allows us to disentangle student and contextual factors affecting achievement. We also account for territorial differences in wealth by means of an index from an external source. The model residuals point out classes with high or low performance. As educational achievement is measured by plausible values, the estimates are obtained through multiple imputation formulas. The results, while confirming the role of traditional student and contextual factors, reveal interesting patterns of achievement in Italian primary schools.