Understanding disparities: Student and school factors associated with U.S. students’ achievement in reading, mathematics, and science

This study examines multilevel associations between student characteristics, school contexts, and academic
achievement across reading, mathematics, and science using PISA 2022 U.S. data. Hierarchical linear models
with 4552 students nested within 154 schools reveal substantial between-school variance, highest for mathematics
(24.5%), followed by science (22.4%) and reading (18.9%). Each of the 10 plausible values per domain
was treated as an imputation of the latent achievement construct, fit on every PV x covariate-imputation combination
(100 fits per model), and pooled via Rubin’s rules. Gender demonstrates domain-specific patterns: males
score 22 points lower in reading but 13 points higher in mathematics, with marginal advantage in science. Home
language and parental education show consistent positive associations across domains. School-mean parental
education and home ICT resources are associated with roughly 3.7 and 11 times the corresponding within-school
effects. Cross-level interactions are significant though random-slopes reveal substantial school-level heterogeneity
in demographic associations.