Educational Psychology

Jee-Seon Kim

Professor, Quantitative Methods Area

jeeseonkim@wisc.edu

(608) 262-0741

1067 Educational Sciences

1025 West Johnson Street

Madison, WI 53706-1706

Kim, Jee-Seon

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Jee-Seon Kim is a Professor in the Department of Educational Psychology at the University of Wisconsin-Madison. Dr. Kim received her BS and MS in Statistics and Ph.D. in Quantitative Psychology. Her research focuses on the development and application of statistical methods for addressing empirical questions in the social and behavioral sciences. Dr. Kim is particularly interested in multilevel modeling, latent variable modeling, causal inference, and unobserved heterogeneity, including methods for modeling change, learning, and human development using longitudinal data and estimating treatment effects and individual differences with clustered observational data using machine learning algorithms.

Education

  • PhD Quantitative Psychology, University of Illinois at Urbana-Champaign, 2001
  • MS Statistics, Ewha Womans University, 1995
  • BA Statistics, Ewha Womans University, 1993

Select Publications

  • Suk, Y., Kim, J.-S., & Kang, H. (2021). Hybridizing machine learning methods and finite mixture models for estimating heterogeneous treatment effects in latent classes. Journal of Educational and Behavioral Statistics, 46, 323-347. Online Publication/Abstract.
  • Molfenter, T., Kim, J.-S., & Zehner, M. (2020). Increasing Engagement in PostWithdrawal Management Services Through a Practice Bundle and Checklist. Journal of Behavioral Health Services & Research Online Publication/Abstract.
  • Suk, Y., Kang, H., & Kim, J.-S. (2020). Random forests approach for causal inference with clustered observational data. Multivariate Behavioral Research Online Publication/Abstract.
  • Kim, J., & Suk, Y. (2019). Specifying multilevel mixture selection models in propensity score analysis. In Wiberg, M., Culpepper, S., Janssen, R., González, J., & Molenaar, D (Eds.), Quantitative Psychology (pp. 279-291). New York, NY: Springer.
  • Bolt, D. M., & Kim, J.-S. (2018). Parameter invariance and skill attribute continuity in the DINA model. Journal of Educational Measurement, 55, 264-280.
  • Kim, J.-S., Lim, W., & Steiner, P. M. (2017). Causal inference with observational multilevel data: Investigating selection and outcome heterogeneity. Quantitative Psychology Research (pp. 287-308). Springer.
  • Kim, J.-S., Steiner, P. M., & Lim, W. C. (2016). Mixture modeling strategies for causal inference with multilevel data. In J. R. Harring, L. M. Stapleton, and S. N. Beretvas (Ed.), Advances in Multilevel Modeling for Educational Research: Addressing Practical Issues Found in Real-World Applications (pp. 335-359). Information Age Publishing.
  • Kim, J.-S., & Steiner, P. M. (2015). Multilevel propensity score methods for estimating causal effects: A latent class modeling strategy. Quantitative Psychology Research, 293-306.
  • Bolt, D. M., Yi, L., & Kim, J. (2014). Measurement and control of response styles using anchoring vignettes: A model-based approach. Psychological Methods, 19, 528-541. Online Publication/Abstract.
  • Kim, J., Anderson, C. J., & Keller, B. S. (2013). Multilevel analysis of assessment data. In L. Rutkowski, M. von Davier, and D. Rutkowski (Ed.), A handbook of international large-scale assessment: Background, technical issues, and methods of data analysis (pp. 389-424). London: Chapman Hall/CRC Press.

Select Presentations

  • Kim, J.-S., & Lyu, W. (2020, July). Classification and Estimation of Heterogeneous Treatment Effects. Paper presented at the International Meeting of Psychometric Society.
  • Kim, J.-S., Suk, Y., & Kang, H. (2019, July). Hybrid random forests for estimating heterogeneous treatment effects. Paper presented at the International Meeting of Psychometric Society, Santiago, Chile.
  • Kim, J.-S. (2018, October). Prediction, Omitted Variables, and Generalized Method of Moments Estimator with Multilevel Data. presented at the Statistics in Insurance Workshop, Madison, WI.
  • Kim, J.-S., Steiner, P. M., & Suk, Y. (2017, July). A unified framework of multilevel matching for causal inference with clustered observational data. Paper presented at the International Meeting of Psychometric Society, Zürich, Switzerland.
  • Kim, J.-S. (2015, July). Causal inference with observational multilevel Data: Challenges & Strategies. presented at the International Meeting of the Psychometric Society, Beijing, China.
  • Kim, J.-S. (2014, November). Multilevel propensity score methods for estimating causal effects. presented at the Advances in Multilevel Modeling for Educational Research: Addressing Practical Issues Found in Real-World Applications, College Park, MD.
  • Kim, J.-S. (2011, May). Integrative data analysis using multilevel modeling. presented at the Integrative Data Analysis: Conceptual Issues and Applied Examples, Washington, DC.

Select Awards and Honors

  • Vilas Faculty Mid-Career Investigator Award, UW Office of the Provost, 2019
  • Fellow, National Academy of Education/Spencer Foundation, 2004