Hierarchical Models

A Basic Introduction to Hierarchical Linear Modeling

March 4, 2024
Mingfeng Xue
by Mingfeng Xue. Hierarchical Linear Modeling (HLM) is an extension of linear models, which offers an approach to analyzing data structures with nested levels. This blog elucidates HLM's significance over traditional linear regression models, particularly in handling clustered data and multilevel predictors. Illustrated with an example from educational research, the blog demonstrates model implementation and interpretation steps. It showcases how HLM accommodates both independent variables from different levels and hierarchical structure data, providing insights into their impacts on the outcome variable. Recommended resources further aid readers in mastering HLM techniques.

James Hall

Consultant
Department of Statistics

James Hall is a graduate student in the Statistics MA program at University of California, Berkeley. He is a husband and father to three awesome kids. Originally from Baltimore, MD, James earned his bachelors in Mathematics at the United States Military Academy at West Point, NY in 2011, and served as a U.S. Army officer. He’s served as a leader at multiple levels within large organizations with a professional focus on visualizing and communicating complex analysis to decision makers. James’ experience and coursework give him expertise in navigating different statistical methods,...

Katherine Wolf

Adjunct Fellow
Environmental Science, Policy, and Management

Doctoral student in Rachel Morello-Frosch's laboratory in the Department of Environmental Science, Policy, and Management working at the intersection of environmental epidemiology, environmental justice, and causal inference. Particularly interested in developing quantitative methods to investigate the operation of social power in environmental monitoring regimes in the United States.