Scholarlink

<p>This study examines the relationship between gender and academic performance across different quantiles among students enrolled in a 3-year STEM (Science, Technology, Engineering, and Mathematics) degree program in Italy. We make use of a unique dataset of linked administrative records, provided through an agreement with the Italian Ministry of University and Research (MUR). The statistical modeling of earned credits presents challenges posed by the discrete and often irregular nature of the observed distribution and the hierarchical structure of our data, which demand an estimation strategy that extends beyond the simplicity of quantile regression. We implement a methodology based on the jittering approach for counts and penalized fixed effects in order to deal with these two distinct extensions over standard quantile regression.</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

Quantile regression approach to analyze gender disparities in STEM university credit distribution

  • 期刊论文

  • 发表日期：

    2025年6月25日

查看全文
  • Riccardo De Santis,
  • Antonella D’Agostino,
  • Nicola Salvati,
  • Francesco Schirripa Spagnolo

摘要

This study examines the relationship between gender and academic performance across different quantiles among students enrolled in a 3-year STEM (Science, Technology, Engineering, and Mathematics) degree program in Italy. We make use of a unique dataset of linked administrative records, provided through an agreement with the Italian Ministry of University and Research (MUR). The statistical modeling of earned credits presents challenges posed by the discrete and often irregular nature of the observed distribution and the hierarchical structure of our data, which demand an estimation strategy that extends beyond the simplicity of quantile regression. We implement a methodology based on the jittering approach for counts and penalized fixed effects in order to deal with these two distinct extensions over standard quantile regression.