Research Interests
My work integrates latent variable modeling with statistical learning to advance statistical and psychometric methods, to address practical problems in educational and psychological testing. Here are a few of my current research interests:
- Theory and methods for complex behavioral data (in particular, sequence data, e.g., log data, natural language data) in assessments;
- Latent variable modeling: missing data, response times, diagnostic classification, statistical computing;
- Longitudinal model for learning and interventions.
Education
Quantitative Psychology, Ph.D., University of Illinois Urbana-Champaign
Applied Mathematics, MS, University of Illinois Urbana-Champaign
Psychology, BA, Bryn Mawr College
Mathematics, BA, Haverford College
Grants
IES R324P230002 (co-PI): Analysis of NAEP Mathematics Process, Outcome, and Survey Data to Understand Test-Taking Behavior and Mathematics Performance of Learners with Disabilities
AERA NSF 112057 (PI): Revision and Review Behavior in Large-Scale Computer-Based Assessments: An Analysis of NAEP Mathematics Process Data
Schmidt Sciences AI Safety Science (co-PI): Creating Effective Benchmarks for LLMs with Human AI Collaboration
Awards and Honors
Alicia Cascallar Award (NCME, 2022)
Excellent Reviewer Award (JEBS, 2020, 2023, 2024)
UIUC List of Teachers Ranked as Excellent by Students (SP 2021, FA 2022, FA 2023, SP 2024)
UIUC LAS Lincoln Excellence for Assistant Professors (LEAP) Scholar (2024 - 2026)
Courses Taught
- PSYC 490 : Measurement and Test Development Lab
- STAT 428: Statistical Computing
- PSYC 593: Statistical Learning for Behavioral Data
- Online workshop on Statistical Learning of Process Data (Video recording)
- Online workshop on R Programming for Data Science
Additional Campus Affiliations
Assistant Professor, Psychology
Assistant Professor, Statistics
Honors & Awards
Alicia Cascallar Award (NCME, 2022)
Excellent Reviewer Award (JEBS, 2021)
UIUC List of Teachers Ranked as Excellent by Students (SP 2021, FA 2022)
Highlighted Publications
Zhang, S., Wang, Z., Qi, J., Liu, J., & Ying, Z. (2023). Accurate Assessment via Process Data. Psychometrika, 88(1), 76–97. https://doi.org/10.1007/s11336-022-09880-8
Fang, G., Guo, J., Xu, X., Ying, Z., & Zhang, S. (2021). Identifiability of Bifactor Models. Statistica Sinica, 31(5), 2309-2330. https://doi.org/10.5705/ss.202020.0386
Xu, X., Fang, G., Guo, J., Ying, Z., & Zhang, S. (2024). Diagnostic Classification Models for Testlets: Methods and Theory. Psychometrika, 89(3), 851-876. https://doi.org/10.1007/s11336-024-09962-9
Zhang, S., Liu, J., & Ying, Z. (2023). Statistical Applications to Cognitive Diagnostic Testing. Annual Review of Statistics and Its Application, 10, 651-675. https://doi.org/10.1146/annurev-statistics-033021-111803
Guo, J., Xu, X., Ying, Z., & Zhang, S. (2022). Modeling Not-Reached Items in Timed Tests: A Response Time Censoring Approach. Psychometrika, 87(3), 835-867. https://doi.org/10.1007/s11336-021-09810-0
Recent Publications
Guo, J., Xu, X., Fang, G., Ying, Z., & Zhang, S. (2025). Jointly modeling responses and omitted items by a competing risk model: A survival analysis approach. British Journal of Mathematical and Statistical Psychology. Advance online publication. https://doi.org/10.1111/bmsp.12382
Wei, X., Zhang, S., & Zhang, J. (2025). Digital Pencil Usage and Mathematics Performance Among Students with Learning Disabilities and Their General Education Peers. Journal of Special Education Technology. Advance online publication. https://doi.org/10.1177/01626434251314041
Zheng, Y., Huang, S., Nydick, S., & Zhang, S. (2025). MxML (Exploring the Relationship between Measurement and Machine Learning): Survey of the Measurement Community. Chinese/English Journal of Educational Measurement and Evaluation. https://doi.org/10.59863/GVZE8492
Du, Y., & Zhang, S. (2024). Detecting Compromised Items With Response Times Using a Bayesian Change-Point Approach. Journal of Educational and Behavioral Statistics. Advance online publication. https://doi.org/10.3102/10769986241290713
Kwon, S., Zhang, S., Köhn, H. F., & Zhang, B. (2024). MCMC stopping rules in latent variable modelling. British Journal of Mathematical and Statistical Psychology, 78(1), 225-257. Advance online publication. https://doi.org/10.1111/bmsp.12357