Scholarlink

<p>This paper studies the relationship between the student abilities in the second year of high school and the infrastructural endowment in all Italian municipalities, using spatial Bayesian modelling. Municipal student scores are obtained by averaging standardised and spatially homogeneous indicators of student outcomes provided by the INVALSI Institute for two subjects: Italian and mathematics. Given the nature of the data, we employ a multilevel regression model assuming a bivariate intrinsic conditionally autoregressive (ICAR) latent effect to explain the spatial variability and account for the correlation between the two subjects. Bayesian model estimation is obtained using the integrated nested Laplace approximation (INLA), implemented in the <Emphasis FontCategory="NonProportional">R-INLA</Emphasis> package. We find that along with a significant association with the current state of school infrastructure and facilities, spatially structured latent effects are still necessary to explain the different student outcomes across municipalities.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Bayesian multilevel bivariate spatial modelling of Italian school data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月3日

查看全文
  • Leonardo Cefalo,
  • Alessio Pollice,
  • Virgilio Gómez - Rubio

摘要

This paper studies the relationship between the student abilities in the second year of high school and the infrastructural endowment in all Italian municipalities, using spatial Bayesian modelling. Municipal student scores are obtained by averaging standardised and spatially homogeneous indicators of student outcomes provided by the INVALSI Institute for two subjects: Italian and mathematics. Given the nature of the data, we employ a multilevel regression model assuming a bivariate intrinsic conditionally autoregressive (ICAR) latent effect to explain the spatial variability and account for the correlation between the two subjects. Bayesian model estimation is obtained using the integrated nested Laplace approximation (INLA), implemented in the R-INLA package. We find that along with a significant association with the current state of school infrastructure and facilities, spatially structured latent effects are still necessary to explain the different student outcomes across municipalities.