Hierarchical models are statistical frameworks that organize data into multiple levels or layers. These models allow researchers to analyze complex data structures, where observations are nested within groups. For example, in educational research, students may be nested within classrooms, which are further nested within schools. This structure helps account for variations at each level. In hierarchical models, parameters can vary across different levels, enabling more accurate predictions and insights. They are commonly used in fields like psychology, sociology, and economics to understand relationships and effects that may differ across groups. This approach enhances the robustness of statistical analyses.