School-level inequality measurement based categorical data: a novel approach applied to PISA
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...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-05-01
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| Series: | Large-scale Assessments in Education |
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| Online Access: | https://doi.org/10.1186/s40536-021-00103-7 |
| Summary: | 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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| ISSN: | 2196-0739 |