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Susu Zhang

Assistant Professor

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 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 

Awards and Honors

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, SP 2024)

UIUC LAS Lincoln Excellence for Assistant Professors (LEAP) Scholar (2024 - 2026)

Courses Taught

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

View all publications on Illinois Experts

Recent Publications

Kwon, S., Zhang, S., Köhn, H. F., & Zhang, B. (2024). MCMC stopping rules in latent variable modelling. British Journal of Mathematical and Statistical Psychology. Advance online publication. https://doi.org/10.1111/bmsp.12357

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, 59(3), 599-619. https://doi.org/10.1080/00273171.2024.2307518

Wei, X., Zhang, S., & Zhang, J. (2024). Identifying student profiles in a digital mental rotation task: insights from the 2017 NAEP math assessment. Frontiers in Education, 9, Article 1423602. https://doi.org/10.3389/feduc.2024.1423602

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, 89(3), 851-876. https://doi.org/10.1007/s11336-024-09962-9

View all publications on Illinois Experts