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

We propose a multivariate multilevel model for the analysis of the Italian sample of the TIMSS&PIRLS 2011 Combined International Database on 4th grade students. The multivariate model jointly considers educational achievement on Reading, Mathematics and Science, thus allowing us to test for differential effects of the covariates on the three scores. This approach represents an advance with respect to official reports, where the three scores are analysed separately. Moreover, the multilevel approach allows us to disentangle student and contextual factors affecting achievement, also considering territorial differences in wealth. The proportion of variability at class level is relevant even after controlling for the observed factors. The residual analysis allows us to locate classes with extremely high or low effectiveness.