Hierarchical Linear Models (HLM) are statistical methods used to analyze data that is organized at more than one level. For example, in educational research, students (level 1) are nested within classrooms (level 2), allowing researchers to examine how both individual and group-level factors influence outcomes. HLM accounts for the dependency of observations within clusters, providing more accurate estimates. These models help in understanding complex relationships by allowing for varying effects at different levels. They can be applied in various fields, including psychology, sociology, and public health, making them valuable for analyzing data with nested structures.