People
Nana Kim
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Pronouns: she, her
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Assistant professor
My interests lie in the development and application of statistical models with the goal of better understanding how students/respondents interact with test items and consequently improving the measurement of psychological and cognitive constructs.
PhD, Educational Psychology (Quantitative Methods), University of Wisconsin-Madison
MA, Education (Measurement & Quantitative Methods), Yonsei University, Seoul
BA, Education, Yonsei University, Seoul
- Educational and psychological measurement
- Item response theory
- (Generalized) mixed effects models
- Response process modeling
- Process data analysis
* I am currently accepting graduate students. Students with research interests that align with mine are encouraged to apply.
* Feel free to reach out for collaboration.
My research interests primarily lie in the development and application of statistical models, such as item response theory (IRT) models, with the goal of better understanding how students (or respondents) interact with test items and consequently improving the measurement of psychological and cognitive constructs. I seek to model and understand psychological and cognitive response processes relevant to solving or responding to test items, especially toward understanding factors or components contributing to individual differences in measured outcomes. My recent projects have focused on exploring the response behavior heterogeneity across individuals in noncognitive assessments (where Likert-type rating scale items are involved), examining the usefulness of response times in understanding response behaviors, and modeling residual item-person interactions unexplained by the IRT model parameters. I am also interested in collaborating with researchers from different areas to investigate more practical issues in education and social/behavioral sciences.
Courses I teach
- EPSY5221: Principles of Educational and Psychological Measurement (F2022, S2024, S2025)
- EPSY8265: Factor Analysis (F2022, F2023)
- EPSY8226: Applications of Item Response Theory Models (S2023, S2024)
- EPSY3264: Basic and Applied Statistics (S2025)
Choi, S.*, McMaster, K. L., & Kim, N. (2025). Toward the fair and valid use of curriculum-based measurement for students with intensive writing needs and linguistically diverse backgrounds. Assessing Writing, 65. https://doi.org/10.1016/j.asw.2025.100948
Kim, N., Deng, J., & Wong, Y. L.* (2025). Digital Module 37: Introduction to item response tree (IRTree) models. Educational Measurement: Issues and Practice, 44, 109-110. https://doi.org/10.1111/emip.12665
Kim, N., Jeon, M., & Partchev, I. (2024). Conditional dependence across slow and fast item responses: With a latent space item response modeling approach. Journal of Intelligence, 12(2), 23. https://doi.org/10.3390/jintelligence12020023
Kim, N., & Bolt, D. M. (2023). Evaluating psychometric differences across fast and slow responses in rating scale measurement. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986231195260

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Educational Psychology
163 Education Sciences Bldg.
56 East River Road
Minneapolis, MN 55455 - nkim530@umn.edu