School of Public Health

Structural Equation Modeling, Latent Variable Modeling, Bayesian Analysis

Structure Equate Model Latent Vari Model18

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 Science23(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.