A Hierarchical and Finite Mixture Modeling Approach to Mathematics Achievement: Evidence from PISA 2022
Year: 2025
Type: article
Abstract: This study investigates the combined effects of cognitive, psychosocial, and cross-subject academic indicators on students’ mathematics achievement across 38 countries that participated in the PISA 2022 creative thinking assessment. Drawing on data from 144,446 students, we employed hierarchical linear modeling to examine how reading and science proficiency, engagement in creative activities (in and out of school), perseverance, curiosity, and socioeconomic status (ESCS) predict mathematical performance. The results show that reading and science are robust predictors of mathematics scores. ESCS and perseverance also demonstrated consistent positive effects, while creativity showed context-specific associations, positive in some clusters and negative in others. To identify latent cross-national typologies, we ap-plied both K-means clustering and Gaussian Mixture Modeling (GMM) to country-level aggregates. Model comparison using the Bayesian Information Criterion (BIC) favored the GMM solution, which was subsequently used to group countries for multigroup structural equation modeling (MG-SEM). Results revealed significant variations in predictor effects across clusters, highlighting heterogeneity in pathways to mathematics success. This study contributes to comparative education research by integrating hierarchical regression and latent classification techniques, offering implications for instructional design and international education policy aimed at promoting mathematical literacy across diverse systems.
DOI: 10.14419/tc3wv638
Institutions: Fu Jen Catholic University, National Chengchi University
Language: English
Subfield: Applied Mathematics
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7117134454
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