Seminar by Prof. Jing OUYANG, Innovation and Information Management at Business school, The University of Hong Kong

International Large-Scale Assessments collect valuable data on educational quality and performance across countries, enabling education systems to share effective techniques and policies. A key analytical tool is the generalized factor model, which measures individuals’ latent traits such as skills and abilities. However, a major challenge arises from Differential Item Functioning (DIF), where different groups (e.g., genders and countries) may have different probabilities of correctly answering the items after controlling for individual latent abilities. To address these challenges, we consider a covariate-adjusted generalized factor model and develop novel and interpretable conditions to address the identifiability issue. Based on the identifiability conditions, we propose a joint maximum likelihood estimation method and establish estimation consistency and asymptotic normality results for the covariate effects under a practical yet challenging asymptotic regime. Furthermore, we derive estimation and inference results for latent factors and the factor loadings. In a related line of work, we propose a novel estimation approach for multi-group DIF analysis that estimates the performance distributions of different groups and produces fair group rankings. The proposed method is applied to PISA 2022 data from the mathematics, science, and reading domains, providing insights into their DIF structures and performance rankings of countries.

Dr. Jing Ouyang is an Assistant Professor of Innovation and Information Management at the Business school of the University of Hong Kong. Prior to joining HKU, Jing received a Ph.D. in Statistics from the University of Michigan and a BSc. in Mathematics and Economics from the Hong Kong University of Science and Technology. Jing is generally interested in latent variable models, psychometrics, high-dimensional statistical inference, and statistical machine learning. Specifically, her research focuses on developing statistical theory, novel methodology and efficient computing tool for latent variable models to analyze high-dimensional and complex data, with interdisciplinary applications in large-scale educational assessments, psychological measurements, and biomedical sciences.