Lower-to-upper secondary school transition: a Bayesian Lasso approach in data modelling

Quality & Quantity(2023)

引用 0|浏览0
暂无评分
摘要
The determinants of transitioning from lower secondary to upper secondary school for Italian and immigrant teenagers (age range: 16–19) were identified by combining data from the European Union Statistics on Income and Living Conditions (EU-SILC) and the Italian Survey on Income and Living Conditions of Families with Immigrants in Italy (IM-SILC) for 2009. A set of individual, family, and contextual characteristics was selected through the Lasso method and a Bayesian approach to explain the decision not to continue on with upper secondary schooling (yes/no). The interruption of this transition revealed a complex pattern. The variables affecting it positively were squared age and almost all the significant first-order interactions, while negative impacts were observed for father’s age, parents’ education level, the amount of optional technological equipment owned, and the occupations of both parents. Other variables entered through the interactions included the individual’s and parents’ self-perceived health conditions, the degree of urbanisation, the type of macro-region, and so on. There were no gender distinctions and differences between Italians and immigrants disappeared when family background and parental characteristics were taken into account.
更多
查看译文
关键词
School-to-work transition, Educational inequality, Parents’ effects on education, Laplace prior distribution, k-Fold cross validation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要