Jing Li

Welcome! I am an assistant professor in the Department of Psychology and Counseling at Pittsburg State University. Feel free to explore my website to learn more about my research, teaching, and academic background.

Research Interests

My research focuses on developing and applying advanced quantitative methods to enhance the validity, fairness, and interpretability of educational and psychological assessments. My methodological expertise includes Rasch measurement theory (RMT), explanatory item response modeling (EIRM), structural equation modeling (SEM), and diagnostic classification modeling (DCM). I apply these methods to examine item functioning, detect subgroup differences, and evaluate the quality of measurement instruments across fields such as education, psychology, and public health.

My recent work also explores the intersection of artificial intelligence and education, including evaluating large language models (LLMs) as raters, examining fairness in AI-based assessment, and leveraging AI-derived item features to predict item difficulty in educational tests.

Representative Publications

Li, J., Yang, X., & Engelhard, G., Jr. (2026). Identification and diagnosis of misreporting in surveys. Educational and Psychological Measurement, 0(0). https://doi.org/10.1177/00131644261451209

Hylick, K., Li, J., Gilliam, S., Sheffield Abdullah, K., & Engelhard, G., Jr. (2026). Examining the psychometric properties of the Perceived Stress Scale in black women during the perinatal period using explanatory Rasch models. Measurement and Evaluation in Counseling and Development, 59(1), 1-19. https://doi.org/10.1080/07481756.2025.2585794

Li, J., Kim, S. H., & Engelhard, G. (2024). Validation of the Household Food Security Survey Module (HFSSM) using factor analysis and Rasch measurement theory. In H. Hwang, H. Wu, & T. Sweet (Eds.), Quantitative psychology: IMPS 2023 (Springer Proceedings in Mathematics & Statistics, Vol. 452). Springer. https://doi.org/10.1007/978-3-031-55548-0_22

Li, W., Zhang, X., Li, J., Yang, X., Li, D., & Liu, Y. (2024). An explanatory study of factors influencing engagement in AI education at the K-12 level: An extension of the classic TAM model. Scientific Reports, 14(1), 13922. https://doi.org/10.1038/s41598-024-64363-3

Li, J., & Engelhard, G. (2023). Using explanatory item response models to evaluate surveys. Journal of Applied Measurement, 24(3/4), 88-101. [ResearchGate PDF]

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Education

University of Georgia, Athens, Georgia

Ph.D. in Educational Psychology, May 2026
Dissertation: Using Explanatory Rasch Models to Evaluate Surveys of Food Insecurity
Committee: George Engelhard Jr. (Chair); Seock-Ho Kim; Zhenqiu (Laura) Lu; Stacey M. Neuharth-Pritchett

M.S. in Statistics, May 2026

Miami University (Ohio), Oxford, Ohio

M.Ed. in Educational Psychology, May 2021
Thesis: A Rasch Analysis of Teaching and Learning International Survey 2013 (Advisor: William Boone)

Michigan State University, East Lansing, Michigan

B.A. in Psychology, May 2018 (Minor in Leadership Organization)

Contact Information