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

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Abstract

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.

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APA

Sempé, L. (2021). School-level inequality measurement based categorical data: a novel approach applied to PISA. Large-Scale Assessments in Education, 9(1). https://doi.org/10.1186/s40536-021-00103-7

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