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迁移与学生表现:采用曲线聚类方法检测地理差异

Migration and students' performance: detecting geographical differences following a curves clustering approach.

作者信息

Boscaino Giovanni, Sottile Gianluca, Adelfio Giada

机构信息

Dipartimento di Scienze Economiche Aziendali e Statistiche, Università degli Studi di Palermo, Palermo, Italy.

出版信息

J Appl Stat. 2020 Nov 9;49(4):1018-1032. doi: 10.1080/02664763.2020.1845624. eCollection 2022.

Abstract

Students' migration mobility is the new form of migration: students migrate to improve their skills and become more valued for the job market. The data regard the migration of Italian Bachelors who enrolled at Master Degree level, moving typically from poor to rich areas. This paper investigates the migration and other possible determinants on the Master Degree students' performance. The Clustering of Effects approach for Quantile Regression Coefficients Modelling has been used to cluster the effects of some variables on the students' performance for three Italian macro-areas. Results show evidence of similarity between Southern and Centre students, with respect to the Northern ones.

摘要

学生的迁移流动是一种新的迁移形式

学生迁移是为了提升自身技能,从而在就业市场上更具价值。这些数据涉及意大利本科毕业生攻读硕士学位时的迁移情况,他们通常从贫困地区迁往富裕地区。本文研究了迁移以及其他可能影响硕士研究生学业表现的因素。分位数回归系数建模的效应聚类方法被用于对意大利三个大区的一些变量对学生学业表现的效应进行聚类分析。结果表明,南部和中部地区的学生与北部地区的学生相比,存在相似性证据。

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