We illustrate a multivariate multilevel analysis in a complex setting of large-scale assessment surveys, dealing with plausible values and accounting for the survey design. We analyse the Italian sample of the TIMSS and PIRLS 2011 Combined International Database on fourth grade students. We jointly considers educational achievement in Reading, Mathematics and Science, thus we test for differential associations of the covariates with the three response variables, and we estimate the residual correlations among pairs of responses within and between classes. 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 data 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
Pennoni, F., Grilli, L., Rampichini, C., Romeo, I. (2017). A multivariate multilevel model to analyze educational achievement in Reading, Mathematics and Science in Italy. Intervento presentato a: Scientific Meeting of the FIRB project on “Mixture and Latent Variable Models for Causal Inference and Analysis of Socio- Economic Data”, Bologna, Italia.
A multivariate multilevel model to analyze educational achievement in Reading, Mathematics and Science in Italy
PENNONI, FULVIAPrimo
;
2017
Abstract
We illustrate a multivariate multilevel analysis in a complex setting of large-scale assessment surveys, dealing with plausible values and accounting for the survey design. We analyse the Italian sample of the TIMSS and PIRLS 2011 Combined International Database on fourth grade students. We jointly considers educational achievement in Reading, Mathematics and Science, thus we test for differential associations of the covariates with the three response variables, and we estimate the residual correlations among pairs of responses within and between classes. 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 data 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
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