ANALYZING IMMIGRANT AND NON-IMMIGRANT BELONGING EXPERIENCES THROUGH IRT IN LARGE-SCALE ASSESSMENTS: INSIGHTS FROM COSTA RICA - Jose Fabian Elizondo-Gonzalez (Istraživanja u Pedagogiji)
Studies across countries part of the Organization for Economic Cooperation and Development (OECD) suggest that learners' sense of school belonging is often influenced by their place of birth. However, large-scale assessment studies rarely explore whether differences in belonging scores between nonimmigrant and immigrant learners are due to test bias. This study fills that gap by examining belonging scores in Costa Rican high schools using the Programme for International Student Assessment (PISA) 2022 data, with a focus on test fairness. Utilizing Multiple-group Differential Item Functioning (DIF) analyses and Item Response Theory (IRT) modeling, results show that all items are DIF-free, confirming no test bias. An Independent Samples t-test reveals no significant differences in belonging scores between immigrant and non-immigrant learners, which is a positive finding. It suggests that Costa Rican educational environments foster a shared sense of belonging, regardless of learners’ place of birth. The most discriminating items, identified through IRT modeling, relate to performative or participatory aspects of school belonging. This study highlights the importance of incorporating IRT modeling and fairness protocols in large-scale assessments. By confirming that sense of belonging is not impacted by place of birth, stakeholders can confidently make decisions that further support inclusive educational practices in Costa Rican classrooms, knowing the PISA sense of belonging index provides unbiased, reliable scores.