Multilevel 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, allowing researchers to understand how both individual and school-level factors influence outcomes. This approach accounts for the hierarchical structure of the data, providing more accurate estimates and insights. This method is particularly useful in fields like education, psychology, and healthcare, where data often comes from nested sources. By considering the relationships between different levels, multilevel models help researchers identify patterns and make informed decisions based on complex data structures.