Multilevel models, also known as hierarchical models, are statistical techniques used to analyze data that have a nested structure. This means that data points can be grouped at different levels, such as students within classrooms or patients within hospitals. These models help researchers understand how variables at different levels influence outcomes, allowing for more accurate predictions and insights. By accounting for the variability at each level, multilevel models can provide a clearer picture of relationships in complex data. They are particularly useful in fields like education, healthcare, and social sciences, where data often comes from different sources or groups that may affect the results.