Multilevel modeling, also known as hierarchical modeling, is a statistical technique used to analyze data that is organized at more than one level. For example, it can be applied to data collected from students within different schools, where individual student performance may be influenced by both personal factors and the characteristics of their school environment. This approach allows researchers to account for the variability at each level, providing more accurate estimates and insights. By considering the nested structure of the data, multilevel modeling helps in understanding how factors at different levels, such as classrooms or communities, interact and affect outcomes.