Structural Equation Modeling, Latent Variable Modeling, Bayesian Analysis
Huh, D., Li, X., Zhou, Z., Walters, S. T., Baldwin, S. A., Tan, Z., … & Mun, E. Y. (2022). A structural equation modeling approach to meta-analytic mediation analysis using individual participant data: Testing protective behavioral strategies as a mediator of brief motivational intervention effects on alcohol-related problems. Prevention Science, 23(3), 390-402.
Kim, S.-Y., Mun, E.-Y., & Smith, S. S. (2014). Using mixture models with known class membership to address incomplete covariance structures in multiple-group growth models. British Journal of Mathematical and Statistical Psychology, 67(1), 94-116.
Kim, S.-Y., Huh, D., Zhou, Z., & Mun, E.-Y. (2020). A comparison of Bayesian to maximum likelihood estimation for latent growth models in the presence of a binary outcome. International Journal of Behavioral Development, 44(5), 447-457.
Mun, E.-Y., von Eye, A., & White, H. R. (2009). An SEM approach for the evaluation of intervention effects using pre-post-post designs. Structural Equation Modeling, 16(2), 315-337.
Tan, Z., Mun, E.-Y., Nguyen, Uyen-Sa, D. T., & Walters, S. T. (2021). Increases in social support co-occur with decreases in depressive symptoms and substance use problems among adults in permanent supportive housing: An 18-month longitudinal study. BMC Psychology, 9(1), 1-13.
Tan, Z., de la Torre, J., Ma, W., Huh, D., Larimer, M. E., & Mun, E.-Y. (2022). A tutorial on
cognitive diagnosis modeling for characterizing mental health symptom profiles using existing item responses. Prevention Science. https://doi.org/10.1007/s11121-022-01346-8.