Multilevel models, also known as hierarchical models, are statistical techniques used to analyze data that is organized at more than one level. For example, data collected from students within different schools can be analyzed to understand both individual student performance and school-level effects. This approach allows researchers to account for the variability at each level, providing more accurate estimates and insights. These models are particularly useful in fields like education, psychology, and healthcare, where data often has a nested structure. By considering the influence of both individual and group-level factors, multilevel models help in understanding complex relationships and improving decision-making based on the findings.