A Cross-Sectional Model Implementing Intersectional Analysis to Identify Extreme Profiles in Invalsi Data

Students have diverse identities and social characteristics. The different combinations of these determinants create a stratification. This study aims to identify the student profiles associated with the highest and lowest academic performance. To this end, we analyse data from the 2022/2023 Invalsi Mathematics test for fifth-grade students. The approach used is the Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), which highlights the relevance of the intersectional nature of social inequalities in shaping academic achievement. The strata are defined by the intersections of gender, 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. 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 a blue-collar job.