Featured Publications
Quan, Y., & Wang, C. (In Press). Using multi-label classification neural networks to detect intersectional DIF. British Journal of Mathematical and Statistical Psychology. Preprint
Introduces a multi-label neural network approach for detecting differential item functioning across intersecting demographic groups in small samples.
Quan, Y., & Wang, C. (2025). Collapsing or not? A practical guide to handling sparse responses for polytomous items. Methodology, 21(1), 46–73. https://doi.org/10.5964/meth.14303
Simulation study demonstrating that category collapse can induce data–model misfit and biased item parameter estimates in polytomous IRT models.
Peer-Reviewed Journal Articles
Quan, Y., & Wang, C. (In Press). Using multi-label classification neural networks to detect intersectional DIF with small sample sizes. British Journal of Mathematical and Statistical Psychology.
Quan, Y., & Wang, C. (2026). Calibrating multidimensional assessments with structural missingness: An application of a multiple-group higher-order IRT model. Applied Psychological Measurement. https://doi.org/10.1177/01466216251415011
Quan, Y., & Wang, C. (2025). Collapsing or not? A practical guide to handling sparse responses for polytomous items. Methodology, 21(1), 46–73. https://doi.org/10.5964/meth.14303
Parker, M., Ciou, S. Y., Quan, Y., Ren, H., Wang, C., & Li, M. (2025). Investigating answer choice bias within a college-level introductory computing assessment. SIGCSE 2026. https://doi.org/10.1145/3770762.3772622
Manuscripts Under Review & In Preparation
Wang, C., Quan, Y., & Arthur, D. (under review). Review of cognitive diagnostic models (CDMs): Recent methodological advancements for addressing challenges in applications. British Journal of Mathematical and Statistical Psychology.
Quan, Y., & Wang, C. (under review). Transitive DIF clustering: A graph-theoretic approach to identifying many-group partial measurement invariance. Psychological Methods.
Park, A., Quan, Y., Lohr, M., & Oxford, M. (in preparation). Concurrent and enduring predictors of preschool language among young children of parents involved in child welfare. Journal of Speech, Language, and Hearing Research.
Working Papers & Preprints
Quan, Y., & Ren, H. (2025). Sample size and assessment length recommendations for the diagnostic status facet model. PsyArXiv. https://doi.org/10.31234/osf.io/zr3je_v1
Selected Conference Presentations
2026
Quan, Y., & Wang, C. (2026, April). Beyond DIF detection: A downstream clustering framework for small sample parameter estimation [Paper Presentation]. National Council on Measurement in Education Annual Meeting, Los Angeles, CA.
Quan, Y., & Wang, C. (2026, April). Detecting small sample Q-matrix misspecification in the diagnostic facet status model [Paper Presentation]. National Council on Measurement in Education Annual Meeting, Los Angeles, CA.
2025
Quan, Y., & Wang, C. (2025, July). A neural network approach to small sample intersectional DIF detection [Paper Presentation]. International Meeting of the Psychometric Society, Minneapolis, MN.
Quan, Y. (2025, May). Using categorical structural equation models to identify facets of student belonging [Paster Presentation]. Community Partner Doctoral Fellowship Research Presentation, Seattle, WA.
Quan, Y., Ren, H., & Wang, C. (2025, March). Constructing a machine learning model for binary predictions with incomplete, imbalanced data and non-linear effects [Paper Presentation]. A Meeting of Methodologists.
Quan, Y., & Sager, M. (2025, February). Identifying core formal assessment competencies and informal learning outcomes of data science and data literacy [Paper Presentation]. Data Science Education K–12: Research to Practice Annual Conference.
2024
Quan, Y., & Wang, C. (2024, November). A new item fit test for the diagnostic facet test model (DFSM) [Paper Presentation]. Pacific Northwest Research on Psychometrics and Applied Statistics Conference, Pullman, WA.
Quan, Y., & Wang, C. (2024, April). Parameter recovery from higher order item response theory models with structural missingness [Paper Presentation]. National Council on Measurement in Education Annual Meeting, Philadelphia, PA.
Quan, Y., & Wang, C. (2024, April). Collapsing or not? A practical guide to handling sparse responses for polytomous items [Paper and Poster Presentation]. American Educational Research Association Annual Meeting, Philadelphia, PA. https://doi.org/10.3102/2102869
2023
Quan, Y., & Wang, C. (2023, April). The effects of sample size and collapse direction on parameter recovery [Paper Presentation]. National Council on Measurement in Education Annual Meeting, Chicago, IL.
Invited Talks & Departmental Seminars
Quan, Y. (2024, October). Item response theory (IRT) model selection and applications. University of Washington Behavioral Research Center for HIV, Seattle, WA.
Quan, Y. (2024, December). Sample size and test length recommendations for the diagnostic status facet model. Center for Statistics and the Social Sciences Student Research Presentation, Seattle, WA.
Quan, Y. (2024, June). Introduction to Bayesian item response theory. Center for Statistics and the Social Sciences Student Research Presentation, Seattle, WA.
Quan, Y. (2024, March). The effects of measurement error on multilevel linear growth model parameter estimates. Center for Statistics and the Social Sciences Student Research Presentation, Seattle, WA.
Quan, Y. (2021, December). Clustering education data using K-medoids with partitioning around the medoids algorithm. Measurement & Statistics Seminar, University of Washington, Seattle, WA.
Thesis
Quan, Y. (2020). A multivariate statistical analysis of major change patterns and significant factors that influence graduation rates: A case study at California State University, Long Beach (Publication No. 28155286) [Master’s thesis, California State University, Long Beach]. ProQuest Dissertations and Theses Global. https://www.proquest.com/dissertations-theses/multivariate-statistical-analysis-major-change/docview/2519029245/se-2