iheo@ou.edu
Dale Hall Tower 739A
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Dr. Heo is always looking for outstanding and motivated students interested in pursuing a Ph.D. in Quantitative Psychology.
Quantitative Psychology
My overarching research goal is to advance flexible modeling approaches for complex social and behavioral data. I specialize in Bayesian statistics, latent variable and mixture modeling, and methods for missing and longitudinal data, with particular attention to their performance under challenging data and modeling conditions. More recently, my work has expanded to emerging data types and the integration of large language models into quantitative methodology.
Heo, I., Jia, F., & Depaoli, S. (2026). Bayesian variable selection via shrinkage priors in growth mixture models. Multivariate Behavioral Research, 61(3), 439–441. https://doi.org/10.1080/00273171.2026.2673279
Depaoli, S., Heo, I., Jauregui, M., Liu, H., & Jia, F. (2026). A comprehensive evaluation of model selection indices for class enumeration in Bayesian latent growth mixture models. Structural Equation Modeling: A Multidisciplinary Journal, 33(2), 157–176. https://doi.org/10.1080/10705511.2025.2566135
Heo, I., Liu, R., Liu, H., Depaoli, S., & Jia, F. (2026). A study of latent state-trait theory framework in piecewise growth models. Applied Psychological Measurement, 50(1–2), 21–32. https://doi.org/10.1177/01466216251360565
Heo, I., Simons, J.-W., & Liu, H. (2025). A tutorial on Bayesian model averaging for exponential random graph models. British Journal of Mathematical and Statistical Psychology. Advance online publication. https://doi.org/10.1111/bmsp.70007
Heo, I., Jia, F., & Depaoli, S. (2025). Recovering knot placements in Bayesian piecewise growth models with missing data. Behavior Research Methods, 57(7), 1–27. https://doi.org/10.3758/s13428-025-02716-0
Liu, H., Heo, I., Ivanov, A., & Depaoli, S. (2025). Model assumption violations in Bayesian latent mediation analysis: An exploration of Bayesian SEM fit indices and PPP. Structural Equation Modeling: A Multidisciplinary Journal, 32(5), 866–896. https://doi.org/10.1080/10705511.2025.2503789
Liu, H., Heo, I., Depaoli, S., & Ivanov, A. (2025). Parameter recovery for misspecified latent mediation models in the Bayesian framework. Structural Equation Modeling: A Multidisciplinary Journal, 32(4), 618–637. https://doi.org/10.1080/10705511.2025.2475490
Heo, I., Depaoli, S., Jia, F., & Liu, H. (2024). Bayesian approach to piecewise growth mixture modeling: Issues and applications in school psychology. Journal of School Psychology, 107, 101366. https://doi.org/10.1016/j.jsp.2024.101366
Heo, I., Jia, F., & Depaoli, S. (2024). Performance of model fit and selection indices for Bayesian piecewise growth modeling with missing data. Structural Equation Modeling: A Multidisciplinary Journal, 31(3), 455–476. https://doi.org/10.1080/10705511.2023.2264514
Depaoli, S., Jia, F., & Heo, I. (2023). Detecting model misspecification in Bayesian piecewise growth models. Structural Equation Modeling: A Multidisciplinary Journal, 30(4), 574–591. https://doi.org/10.1080/10705511.2022.2144865
Liu, R., Heo, I., Liu, H., Shi, D., & Jiang, Z. (2023). Applying negative binomial distribution in diagnostic classification models for analyzing count data. Applied Psychological Measurement, 47(1), 64–75. https://www.doi.org/10.1177/01466216221124604
