Multilevel Intersectional Analysis to Identify Extreme Profiles in Italian Student Achievement Data

Enrico Contin and Leonardo Grilli ()
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Enrico Contin: Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, 50134 Florence, Italy
Leonardo Grilli: Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, 50134 Florence, Italy

Social Sciences, 2025, vol. 14, issue 11, 1-17

Abstract: Students have diverse identities and social characteristics. The different combinations of these factors create a stratification that affects the learning outcomes. This study aims to identify the student profiles associated with the highest and lowest academic performance. To this end, we analyse data from the 2022/23 INVALSI Mathematics test for fifth-grade students. The approach used is the Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), which highlights the intersectional nature of social inequalities in shaping academic achievement. The strata are defined by the intersections of sex, origin, family environment, parental education, and parental occupation. Moreover, recognising the critical role of the school context, we fit a cross-classified multilevel model with random effects for both intersectional strata and schools. Indeed, model fitting reveals that the school-level variance is substantial, being about three-fourths of the variance due to the intersectional strata. The results show that the lowest-performing students are characterised by an unfavourable family environment, parents with compulsory or unknown education, and parents who are unemployed or in blue-collar jobs.

Keywords: large-scale assessment data; MAIHDA; random effects; schools; test scores (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2025
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