Multilevel modeling is a statistical methodology designed to analyze data structured in multiple hierarchical levels, explicitly modeling variation at each level of aggregation to account for dependencies within clusters and investigate relationships that span across these levels. This approach is significant for providing more accurate parameter estimates and understanding complex phenomena where observations are nested.