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In statistics, hierarchical linear modeling (HLM), also known as multi-level analysis, is a more advanced form of simple linear regression and multiple linear regression. Multilevel analysis allows variance in outcome variables to be analysed at multiple hierarchical levels, whereas in simple linear and multiple linear regression all effects are modeled to occur at a single level. Thus, HLM is appropriate for use with nested data.

For example, in educational research, data is often considered as pupils nested within classrooms nested within schools. In organizational psychology research, data from individuals must often be nested within teams or other functional units. For repeated measures data, time can be considered as another level which occurs within participants.

Multilevel analysis has been extended to include multilevel structural equation modeling, multilevel latent class modeling, and other more general models.

See also

• Multilevel model
• Restricted randomization

External links

Books

• Hierarchical Linear Models (Second Edition). Thousand Oaks: Sage Publications, 2002. Stephen Raudenbush and Anthony Bryk.
• Applied Longitudinal Data Analysis (Singer & Willett, 2003)
• Joop Hox: Book on Multilevel Analysis

Software

• “HLM” — Proprietary software
• MLwiN software
• Don Hedeker: Free Multilevel Software

Other resources

• Centre for Multilevel Modelling
• Tom Snijders' Multilevel Analysis Page
• HLM Examples and Resources