Jing Ouyang, 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 Item Response Theory (IRT) 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. Ignoring or improperly accounting for DIF when calibrating the IRT model can lead to severe biases in the estimated performance distributions, which may further distort the ranking of the groups. Unfortunately, existing methods cannot guarantee the statistically consistent recovery of the group ranking without unrealistic assumptions for ILSA, such as the existence and knowledge of reference groups and anchor items. To fill this gap, we propose new approaches to DIF analysis across multiple groups. The approaches are computationally efficient and statistically consistent, without making strong assumptions about reference groups and anchor items. The proposed methods are applied to PISA 2022 data from the mathematics, science, and reading domains, providing insights into their DIF structures and performance rankings of countries.

About the Speaker

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 PhD 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, Jing’s 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.