School-level inequality measurement based categorical data: a novel approach applied to PISA

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This paper introduces a new method to measure school-level inequality based on Item Response Theory (IRT) models. Categorical data collected by large-scale assessments poses diverse methodological challenges hinder measuring inequality due to data truncation and asymmetric intervals between categories. I use family possessions data from PISA 2015 to exemplify the process of computing the measurement and develop a set of country-level mixed-effects linear regression models comparing the predictive performance of the novel inequality measure with school-level Gini coefficients. I find school-level inequality is negatively associated with learning outcomes across many non-European countries.
Original languageEnglish
Article number9
JournalLarge-Scale Assessments in Education
Issue number1
Publication statusPublished - 3 May 2021


  • Inequality
  • Item Response Theory
  • Ordinal data
  • PISA
  • School inequality

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