Imputing PIRLS’s home socioeconomic status from students’ and schools’ questionnaire data: a simulation with MICE

International large-scale assessments (ILSAs) like TIMSS, PIRLS, and PISA use planned missingness completely at random (MCAR) but suffer from non-response bias with missing data not at random (MNAR), especially in home questionnaires where parental omissions may correlate with item content, compromising cross-country comparisons. This study evaluated whether PIRLS 2021’s Home Socioeconomic Status (HSES) could be reliably imputed using Multivariate Imputation by Chained Equations (MICE) based on student and school data. Six methods—Predictive Mean Matching (PMM), Weighted PMM, CART, Random Forests, Bayesian Linear Regression, and Lasso—were evaluated on 5,500 students with 10–60% simulated missingness. Imputation accuracy declined from 36% explained variance (correlation ∼0.6) at 10% missingness to 16% (correlation <0.4) at 60% missingness. PMM performed best but exhibited shrinkage, overestimating low HSES and underestimating high HSES. Results underscore the critical need to address MNAR in ILSAs to ensure equitable and reliable international educational comparisons.