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Justin Kern


I am currently an assistant professor in the Department of Educational Psychology. I received my Ph.D. in quantitative psychology at UIUC in 2017. I spent the 2017-2018 academic year as a visiting assistant professor of quantitative psychology at the University of California, Merced, before returning to UIUC in Fall 2018. My primary area of research is in educational and psychological measurement. More broadly, I am interested in the improvement of methods used for analyzing multivariate data in the behavioral sciences as a whole. As a methodologist, I work collaboratively with others in education, psychology, and other behavioral sciences around the UIUC community.

Key Professional Appointments

  • Assistant Professor, Educational Psychology, University of Illinois, Urbana-Champaign

Ph.D. in Quantitative Psychology, University of Illinois at Urbana-Champaign, 2017.

M.A. in Psychology, University of Illinois at Urbana-Champaign, 2013.

M.S. in Statistics, University of Illinois at Urbana-Champaign, 2012.

B.S. in Psychology and Mathematics, Central Michigan University, 2009.

Research & Service

My scholarship is focused primarily on issues concerning measurement in psychology and education. The field of measurement involves the creation of mathematical and technical solutions to many highly practical goals. For instance, measurement has a great impact on society through its use in selecting, classifying, and diagnosing people. Furthermore, measurement allows researchers to test theories regarding such societally important traits that would otherwise be unobservable, such as abilities, attitudes, and personality.

As a member of the measurement field, my research seeks to produce and investigate quantitative methodologies useful for the modeling of psychological and educational traits, the construction of tests and measurement tools, and the investigation of behavioral data. This is primarily through the lens of item response theory (IRT). My scholarship can be summarized in terms of three themes: 1) using response times in test construction; 2) investigating models (four-parameter IRT models) used to account for guessing and slipping in responses; and 3) modeling response processes using tree-based IRT models.


Corr, C., Love, H., Snodgrass, M. R., Kern, J. L., & Chudzik, M. (2023). Methodological Training in Special Education Doctoral Programs: A Mixed-Methods Exploration. Teacher Education and Special Education, 46(2), 108-126.  link >

Napolitano, C. M., Kern, J. L., & Freund, A. M. (2022). The Backup Planning Scale (BUPS): A Brief, Self-Reported Measure of a Person’s Tendency to Develop, Reserve, and Use Backup Plans. Journal of Personality Assessment, 104(4), 496-508.  link >

Curtiss, S. L., McBride, B. A., Uchima, K., Laxman, D. J., Santos, R. M., Weglarz-Ward, J., & Kern, J. (2021). Understanding Provider Attitudes Regarding Father Involvement in Early Intervention. Topics in Early Childhood Special education, 41(2), 147-159.  link >

Kern, J. L., & Choe, E. (2021). Using a Response Time–Based Expected A Posteriori Estimator to Control for Differential Speededness in Computerized Adaptive Test. Applied Psychological Measurement, 45(5), 361-385.  link >

Parthasarathy, R., Garfield, M., Rangarajan, A., & Kern, J. L. (2021). The case of organizational innovation capability and health information technology implementation success: As you sow, so you reap? International Journal of Healthcare Information Systems and Informatics, 16(4).  link >

Zheng, C., Guo, S., & Kern, J. L. (2021). Fast Bayesian Estimation for the Four-Parameter Logistic Model (4PLM). SAGE Open, 11(4).  link >

Du, Y., & Kern, J. L. (2020). The four-parameter normal ogive model with response times. In M. Wiberg, D. Molenaar, J. González, U. Böckenholt, & J-S. Kim (Eds.), Quantitative Psychology - 84th Annual Meeting of the Psychometric Society, IMPS 2019 (pp. 55-67). (Springer Proceedings in Mathematics and Statistics; Vol. 322). Springer.  link >

Guo, S., Zheng, C., & Kern, J. L. (2020). IRTBEMM: An R Package for Estimating IRT Models With Guessing or Slipping Parameters. Applied Psychological Measurement, 44(7-8), 566-567.  link >

Kern, J. L., & Culpepper, S. A. (2020). A Restricted Four-Parameter IRT Model: The Dyad Four-Parameter Normal Ogive (Dyad-4PNO) Model. Psychometrika, 85(3), 575-599.  link >

Culpepper, S. A., Aguinis, H., Kern, J. L., & Millsap, R. (2019). High-Stakes Testing Case Study: A Latent Variable Approach for Assessing Measurement and Prediction Invariance. Psychometrika, 84(1), 285-309.  link >

Koehn, H. F., & Kern, J. L. (2019). Additive Trees for Fitting Three-Way (Multiple Source) Proximity Data. In M. Wiberg, S. Culpepper, R. Janssen, J. González, & D. Molenaar (Eds.), Quantitative Psychology - 83rd Annual Meeting of the Psychometric Society, 2018 (pp. 403-413). (Springer Proceedings in Mathematics and Statistics; Vol. 265). Springer.  link >

Choe, E. M., Kern, J. L., & Chang, H. H. (2018). Optimizing the Use of Response Times for Item Selection in Computerized Adaptive Testing. Journal of Educational and Behavioral Statistics, 43(2), 135-158.  link >

Weglarz-Ward, J. M., Santos, R. M., Laxman, D. J., McBride, B. A., Curtiss, S. L., & Kern, J. L. (2018). Inviting Dad to the Dance: Father-Child Involvement and Interactions. In J. A. McCollum, R. M. Santos, & J. M. Weglarz-Ward (Eds.), Interaction: Enhancing Children’s Access to Responsive Interactions (pp. 51-64). (DEC Recommended Practices Monograph Series; No. 5). Division for Early Childhood of the Council for Exceptional Children.

Hur, J., Heller, W., Kern, J. L., & Berenbaum, H. (2017). A Bi-Factor Approach to Modeling the Structure of Worry and Rumination. Journal of Experimental Psychopathology, 8(3), 252-264.  link >

Kern, J. L. (2017). On the Correspondence Between Procrustes Analysis and Bidimensional Regression. Journal of Classification, 34(1), 35-48.  link >

McBride, B. A., Curtiss, S. J., Uchima, K., Laxman, D. J., Santos, R. M., Weglarz-Ward, J., Dyer, W. J., Jeans, L. M., & Kern, J. (2017). Father Involvement in Early Intervention: Exploring the Gap Between Service Providers’ Perceptions and Practices. Journal of Early Intervention, 39(2), 71-87.  link >

Kern, J. L., McBride, B. A., Laxman, D. J., Justin Dyer, W., Santos, R. M., & Jeans, L. M. (2016). The role of multiple-group measurement invariance in family psychology research. Journal of Family Psychology, 30(3), 364-374.  link >

Laxman, D. J., McBride, B. A., Jeans, L. M., Dyer, W. J., Santos, R. M., Kern, J. L., Sugimura, N., Curtiss, S. L., & Weglarz-Ward, J. M. (2015). Father Involvement and Maternal Depressive Symptoms in Families of Children with Disabilities or Delays. Maternal and child health journal, 19(5), 1078-1086.  link >


EPSY 582: Advanced Statistical Methods (EPSY 582) Advanced topics in analyses of variance and covariance, and principles of experimental design; brief introduction to multivariate analysis, including rudiments of matrix algebra.

EPSY 586: Theories of Measurement II (EPSY 586) Provides a conceptual framework of Item Response Theory (IRT) and its applications. Students will learn the techniques and theory of IRT and apply the methods to educational and psychological assessments. Topics covered include both dichotomous and polytomous IRT modelling, item structure and latent traits estimation, modeling and detecting Differential Item Functioning, linking and equating, computer adaptive testing, dimensionality testing, and cognitive diagnosis.

EPSY 590: Advanced Seminar in Educational Psychology (EPSY 590) Seminar in educational psychology; topics relate to the areas of specialization represented by the various divisions within the department.