Research Interests
I study how measurement theory and psychometrics can guide the assessment of both human (e.g., psychological traits, knowledge and skills) and machine (e.g., AI model capability). My research identifies practical questions in these domains that cannot be adequately addressed with existing statistical tools, and I develop new statistical tools that better address them.
My current research interests include:
- Developing measurement theory and psychometric tools for unstructured test response data (e.g., process data, constructed responses);
- Adapting LLMs to support development of measurement theory-grounded, evidence-centered assessments for learning (e.g., diagnostic assessments, simulation-based tasks);
- Measurement theory and new psychometric tools for AI model evaluation and benchmark design.
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 Foundation AI Safety Science Grant (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, SP 2025)
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
- STAT 410: Statistics and Probability II
- Online workshop on Statistical Learning of Process Data (Video recording)
- Online workshop on R Programming for Data Science
Additional Campus Affiliations
Associate Professor, Psychology
Associate Professor, Statistics
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
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
Kwon, S., & Zhang, S. (2025). Explaining Performance Gaps with Problem-Solving Process Data via Latent Class Mediation Analysis. Psychometrika. Advance online publication. https://doi.org/10.1017/psy.2025.10038
Xiao, Z., Zhang, S., Lai, V., & Liao, Q. V. (2023). Evaluating Evaluation Metrics: A Framework for Analyzing NLG Evaluation Metrics using Measurement Theory. In H. Bouamor, J. Pino, & K. Bali (Eds.), EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 10967-10982). (EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.emnlp-main.676
Recent Publications
Domingue, BW., Braginsky, M., Caffrey-Maffei, L., Gilbert, JB., Kanopka, K., Kapoor, R., Lee, H., Liu, Y., Nadela, S., Pan, G., Zhang, L., Zhang, S., & Frank, MC. (2025). An introduction to the Item Response Warehouse (IRW): A resource for enhancing data usage in psychometrics. Behavior Research Methods, 57(10), Article 276. https://doi.org/10.3758/s13428-025-02796-y
Du, Y., & Zhang, S. (2025). Detecting Compromised Items With Response Times Using a Bayesian Change-Point Approach. Journal of Educational and Behavioral Statistics, 50(2), 296-330. https://doi.org/10.3102/10769986241290713
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, 78(3), 804-829. https://doi.org/10.1111/bmsp.12382
Kwon, S., & Zhang, S. (2025). Explaining Performance Gaps with Problem-Solving Process Data via Latent Class Mediation Analysis. Psychometrika. Advance online publication. https://doi.org/10.1017/psy.2025.10038
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, 40(4), 443-455. https://doi.org/10.1177/01626434251314041