Hierarchical Linear Modeling (HLM) 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 classrooms or schools, allowing researchers to understand how both individual and group-level factors influence outcomes. HLM accounts for the nested structure of the data, providing more accurate estimates of relationships. This modeling approach helps in examining variations at different levels, such as individual differences among students and broader influences from their classrooms or schools. By doing so, HLM can reveal insights that traditional methods might overlook, making it a valuable tool in fields like education and social sciences.