Research Interests
I work on the integration of latent variable modeling and statistical learning to advance statistical and psychometric methods addressing 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 R324P210005 (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
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
Honors & Awards
Alicia Cascallar Award (NCME, 2022)
Excellent Reviewer Award (JEBS, 2020, 2023)
UIUC List of Teachers Ranked as Excellent by Students (SP 2021, FA 2022, FA 2023)
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. Advance online publication. 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
Ulitzsch, E., Zhang, S., & Pohl, S. (2024). A Model-Based Approach to the Disentanglement and Differential Treatment of Engaged and Disengaged Item Omissions. Multivariate Behavioral Research. Advance online publication. https://doi.org/10.1080/00273171.2024.2307518
Xu, X., Zhang, S., Guo, J., & Xin, T. (2024). Biclustering of Log Data: Insights from a Computer-Based Complex Problem Solving Assessment. Journal of Intelligence, 12(1), Article 10. https://doi.org/10.3390/jintelligence12010010
Xu, X., Fang, G., Guo, J., Ying, Z., & Zhang, S. (2024). Diagnostic Classification Models for Testlets: Methods and Theory. Psychometrika. Advance online publication. https://doi.org/10.1007/s11336-024-09962-9
Zhang, S., Tang, X., He, Q., Liu, J., & Ying, Z. (2024). External Correlates of Adult Digital Problem-Solving Process: An Empirical Analysis of PIAAC PSTRE Action Sequences. Zeitschrift fur Psychologie / Journal of Psychology, 232(2), 120-136. https://doi.org/10.1027/2151-2604/a000554
Zhang, B., Luo, J., Zhang, S., Sun, T., & Zhang, D. C. (2024). Improving the Statistical Performance of Oblique Bifactor Measurement and Predictive Models: An Augmentation Approach. Structural Equation Modeling, 31(2), 233-252. https://doi.org/10.1080/10705511.2023.2222229