{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"Research Square"}],"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T16:38:56Z","timestamp":1758040736087,"version":"3.44.0"},"posted":{"date-parts":[[2025,9,11]]},"group-title":"In Review","reference-count":57,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,8,1]]},"abstract":"<title>Abstract<\/title>\n        <p>Background\nBrazil\u2019s participation in PISA 2022 offers a unique opportunity to explore how learning outcomes vary across regions within a decentralized education system.\nObjective\nWe evaluate whether PISA 2022 can generate statistically reliable state-level mathematics estimates in Brazil and identify key student-, school-, and state-level drivers of performance.\nMethods\nWe analysed mathematics scores from 10,798 fifteen-year-olds across 598 schools in all 26 Brazilian states and the Federal District. To assess concurrent validity, state-level PISA means were regressed on mean scores from the 2021 Sistema de Avalia\u00e7\u00e3o da Educa\u00e7\u00e3o B\u00e1sica (SAEB). We then applied a three-level hierarchical linear model\u2014students nested within schools, nested within states\u2014adding predictors stepwise.\nResults\nState PISA means accounted for 64% of the variance in SAEB scores (R\u00b2 = 0.64), showing strong concurrent validity with the national grade 9 assessment. At the student level, mathematics preference (\u03b2\u2009=\u20090.44), anticipated effort under grading (\u03b2\u2009=\u20090.18), and socioeconomic status (\u03b2\u2009=\u20090.14) were the strongest positive predictors, while grade repetition had the largest negative effect (\u03b2 = \u2212\u20090.43). Attending a private school conferred an advantage equivalent to approximately four years of schooling. At the state level, after controlling for variables at all levels, only the municipal Human Development Index (HDIm; \u03b2\u2009=\u20090.13) remained statistically significant.\nConclusions\nPISA 2022 illuminates sub-national indicators of Brazilian educational quality and uncovers globally recognized, multilayered determinants of mathematics performance. Routinely integrating ILSAs with national assessments can help inform equity-oriented education policies.<\/p>","DOI":"10.21203\/rs.3.rs-7273143\/v1","type":"posted-content","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T09:41:39Z","timestamp":1757583699000},"source":"Crossref","is-referenced-by-count":0,"title":["Beyond the Average: Mapping Educational Inequality in Brazil with PISA 2022"],"prefix":"10.21203","author":[{"given":"Lu\u00edsa","family":"Cito","sequence":"first","affiliation":[{"name":"Universidade Lus\u00f3fona"}]},{"given":"Jo\u00e3o","family":"Mar\u00f4co","sequence":"additional","affiliation":[{"name":"Universidade Lus\u00f3fona"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/978-1-4020-8427-0_13","article-title":"Multilevel Modeling with HLM: Taking a Second Look at PISA","author":"Anderson JO","year":"2009","unstructured":"Anderson, J. O., Milford, T., & Ross, S. P. (2009). 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