Hierarchical modeling is a statistical approach that organizes data into multiple levels or layers. This method allows researchers to analyze complex data structures, where observations are nested within groups. For example, in a study of students within schools, each student can be influenced by both individual characteristics and the broader context of their school. This modeling technique helps to account for variability at different levels, improving the accuracy of predictions. By considering both individual and group-level factors, hierarchical models can provide insights into how different influences interact, making them valuable in fields like education, psychology, and sociology.