Traditional statistical methods often fall short when dealing with hierarchical data because they ignore the dependency of observations within clusters. MLM addresses this issue by considering both fixed effects (overall average effects) and random effects (cluster-specific deviations). This dual consideration provides more accurate and generalizable insights, making MLM particularly useful for studying complex public health issues such as disease transmission and health disparities.
