Stats, Volume 9, Issue 2
2026 April - 24 articles
Cover Story: This paper introduces MAI-GAN, a hybrid framework for generating synthetic longitudinal multilevel data while preserving the inferential structure of the original dataset. By combining Bayesian longitudinal MAIHDA with conditional GAN-based residual generation, the method targets fixed effects, variance components, and variance partitioning coefficients central to intersectional multilevel analysis. Applied to repeated-measures biology education data, MAI-GAN reproduced key inferential quantities across multiple training seeds, offering a principled approach to privacy-preserving data sharing and methodological validation. View this paper
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Stats - ISSN 2571-905X