Leonardo Grilli|Fulvia Pennoni|Carla Rampichini|Isabella Romeo
Exploiting TIMSS and PIRLS combined data: Multivariate multilevel modelling of student achievement
University of Milano-Bicocca|Statistical Service|University of Florence|University of Milano-Bicocca|University of Milano-Bicocca|University of Florence|Statistical Service|University of Milano-Bicocca
illustrate perform multivariate multilevel analysis complex setting largescale assessment surveys dealing plausible values accounting survey designin particular consider italian sample timsspirls 2011 combined international database fourth grade studentsthe multivariate approach jointly considers educational achievement reading mathematics science thus allowing us test differential associations covariates three outcomes estimate residual correlations among pairs outcomes within classesmultilevel modelling allows us disentangle student contextual factors affecting achievementwe also account territorial differences wealth means index external data sourcethe model residuals point classes high low performanceas educational achievement measured plausible values estimates obtained multiple imputation formulas introductionthe role largescale assessment surveys public debate education dramatically grown since mid1980sdespite inevitable criticism international achievement testing merit display great variability educational systems across world shed light process underlying growth human capitalas discussed rutkowski von davier rutkowski 2014 international achievement testing education many ambitious purposes including assessment policies practicesindeed understanding determinants achievement compulsory school extremely important design interventions level see among others reeve jang 2006 references thereinin paper consider largescale assessment surveys timss trends international mathematics science study pirls progress international reading literacy study focusing italian datathese surveys generally carried different years 2011 first time two cycles