Yale Quan
Assistant Professor
Bio
Dr. Yale Quan is an Assistant Professor of Education at the University of Southern Mississippi. His methodological research focuses on developing psychometric models that integrate advanced machine learning approaches with item response theory and diagnostic classification models to support culturally responsive measurement. Much of his work focuses on small-sample and high-dimensional inference, and many-group measurement invariance. He is particularly interested in the use of neural networks and graph-based methods to improve DIF detection, IRT parameter estimation, and scoring. Dr. Quan also partners with K–12 schools and community organizations to study student belonging and chronic absenteeism. Dr. Quan earned a Ph.D. in Measurement and Statistics from the University of Washington, and his research has appeared in Psychological Methods, the British Journal of Mathematical and Statistical Psychology, and Methodology.
- PHD - University of Washington-Seattle Campus (2026)
- MS - California State University-Long Beach (2020)
- BS - California State University-Long Beach (2013)
- Using multilabel classification neural network to detect intersectional DIF with small sample sizes, BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY, 2026, 10.1111/bmsp.70041
- Review of cognitive diagnostic models (CDMs): Recent methodological advancements for addressing practical challenges, BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY, 2026, 10.1111/bmsp.70066
- Transitive Differential Item Functioning Clustering: A Graph-Theoretic Approach to Identifying Many-Group Partial Measurement Invariance, PSYCHOLOGICAL METHODS, 2026, 10.1037/met0000873
- Collapsing or Not? A Practical Guide to Handling Sparse Responses for Polytomous Items, METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES, 2025, 10.5964/meth.14303