利用TIMSS和PIRLS组合数据：学生成绩的多元多水平模型
Exploiting TIMSS and PIRLS combined data: multivariate multilevel modelling of student achievement
摘要：
我们利用一个多变量多层次模型的TIMSS\&PIRLS 2011年联合国际数据库的四年级学生的意大利样本的分析。多变量方法共同考虑了阅读，数学和科学的教育成就，从而使我们能够测试协变量与三个结果的差异关联，并估计学生和班级水平的结果对之间的残差相关性。多层次模型使我们能够解开学生和影响成绩的背景因素。我们还通过外部来源的指数来说明财富的地区差异。模型残差指出具有高或低性能的类。由于教育成就是通过合理的值来衡量的，因此通过多重插补公式获得估计值。结果，同时确认传统的学生和背景因素的作用，揭示了有趣的模式，在意大利小学的成就。
英文摘要：
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.
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