Multilevel modeling is a statistical technique used to analyze data that has a hierarchical or nested structure, such as individuals nested within groups or repeated measurements over time. It allows researchers to account for the correlations and dependencies between the observations within each level of the hierarchy, and to estimate both within-group and between-group effects simultaneously. Multilevel modeling is widely used in social sciences, education, health, and other fields to study complex relationships and interactions between variables at different levels of analysis.