弱势群体
劣势
倾向得分匹配
匹配(统计)
Probit模型
数学教育
班级(哲学)
多级模型
心理学
政治学
经济增长
计量经济学
经济
计算机科学
数学
统计
人工智能
法学
作者
Tommaso Agasisti,Mara Soncin,Riccardo Valenti
出处
期刊:Policy Studies
[Taylor & Francis]
日期:2016-01-20
卷期号:37 (2): 147-177
被引量:9
标识
DOI:10.1080/01442872.2015.1127341
摘要
This research investigates a particular category of disadvantaged students, namely those who are able to overcome a situation of socio-economical disadvantage and obtain good academic results (here named 'resilient students'). We have used micro-data provided by the Italian National Evaluation Committee for Education and focused on class and school-level characteristics that help disadvantaged students to become resilient students when they move from primary (grade 5) to lower secondary school (grade 6), concentrating our analysis on four major cities. We employed a probit regression and a propensity score matching model, finding that class and school factors do matter. In particular, we looked at whether the performance of their peers has a positive impact on that of disadvantaged students, estimating the increased probability of these students becoming resilient.
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