Once overdispersion is identified, several strategies can be employed to address it:
Use of alternative models: Models such as the negative binomial distribution or zero-inflated models can better accommodate the extra variability. Inclusion of covariates: Adding relevant covariates to the model can help account for unmeasured variability. Random effects models: These models incorporate random variation at different levels (e.g., individual, group) and can help address overdispersion due to unobserved heterogeneity. Data transformation: Transforming the data, such as using a log-transformation, can sometimes stabilize the variance.
