Articles
This page provides a list of peer-reviewed journal articles I have (co)authored in both methodological and applied journals. For potential collaborations or to discuss research ideas, please reach out to me via email.
In press
- Liu, H., Heo, I., Ivanov, A., & Depaoli, S. (in press). Model assumption violations in Bayesian latent mediation analysis: An exploration of Bayesian SEM fit indices and PPP. Structural Equation Modeling: A Multidisciplinary Journal.
2025
- 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. Advance online publication. https://doi.org/10.1080/10705511.2025.2475490
- Heo, I., Pfadt, J. M., & Wagenmakers, E.–J. (2025). Contributed discussion of “Sparse Bayesian factor analysis when the number of factors is unknown”. Bayesian Analysis, 20(1). 295–296. https://doi.org/10.1214/24-BA1423
2024
- 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
2023
- 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
- Heo, I., Jia, F., & Depaoli, S. (2023). Book review of Longitudinal structural equation modeling with Mplus: A latent state-trait perspective by Geiser. Psychometrika, 88(2), 733–737. https://doi.org/10.1007/s11336-022-09897-z
- 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