School of Public Health

Categorical Data Analysis

Categorical Data Analysis2
Categorical data analysis involves analysis of variables measured in nominal or ordinal scales or count variables. Categorical variables can be in the predictor, outcome, or both sides. Ideas for graphic tiles can be one of the typical distributions with a spike around zero and over dispersion.

Huh, D., Mun, E.-Y., Walters, S. T., Zhou, Z., & Atkins, D. C. (2019). A tutorial on individual participant data meta-analysis using Bayesian multilevel modeling to estimate alcohol intervention effects across heterogeneous studies. Addictive Behaviors, 94, 162-170.

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.

LoParco, C. R., Zhou, Z., Fairlie, A. M., Litt, D. M., Lee, C. M., & Lewis, M. A. (2021). Testing daily-level drinking and negative consequences as predictors of next-day drinking cognitions. Addictive behaviors122, 107042.

von Eye, A., & Mun, E.-Y. (2013). Log-linear modeling: Concepts, interpretation, and applications. New York: Wiley.

von Eye, A., Mair, P., & Mun, E.-Y. (2010). Advances in Configural Frequency Analysis. New York: The Guilford Press.

von Eye, A., Mun, E.-Y., & Bogat, G. A. (2008). Temporal patterns of variable relationships in

person-oriented research – Longitudinal models of Configural Frequency Analysis. Developmental Psychology, 44(2), 437-445.

Zhou, Z., Li, D., & Zhang, S. (2022). Sample size calculation for cluster randomized trials with zero‐inflated count outcomes. Statistics in Medicine41(12), 2191-2204.

Zhou, Z., Xie, M., Huh, D., & Mun, E. Y. (2021). A bias correction method in meta‐analysis of randomized clinical trials with no adjustments for zero‐inflated outcomes. Statistics in medicine40(26), 5894-5909.