Encyclopedia of Behavioral Medicine

Living Edition
| Editors: Marc Gellman

Hierarchical Linear Modeling (HLM)

  • Yutaka Matsuyama
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6439-6_407-2



Hierarchical linear modeling (HLM) is a particular regression model that is designed to take into account the hierarchical or nested structure of the data. HLM is also known as multilevel modeling, linear mixed-effects model, or covariance components model (Leyland and Goldstein 2001).


HLM has historically been used in educational research where hierarchies occur naturally: students nested within classrooms, classrooms nested within schools, and schools nested within districts (Sullivan et al. 1999). Recent advances in statistical computing capabilities have made this model more available to researchers across a variety of disciplines. For example, in organizational psychology research, data from individuals must often be nested within teams or other functional units. For repeated measures or longitudinal data, time can be considered as another level...

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References and Further Reading

  1. Austin, P. C., Yu, J. V., & Alter, D. A. (2003). Comparing hierarchical modeling with traditional logistic regression analysis among patients hospitalized with acute myocardial infarction: Should we be analyzing cardiovascular outcomes data differently? American Heart Journal, 145, 27–35.  https://doi.org/10.1067/mhj.2003.23.CrossRefPubMedGoogle Scholar
  2. Cnaan, A., Laird, N. M., & Slasor, P. (1997). Tutorial in biostatistics: Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Statistics in Medicine, 16, 2349–2380.  https://doi.org/10.1002/(SICI)1097-0258(19971030)16:20<2349::AID-SIM667<3.0.CO;2-E.CrossRefPubMedGoogle Scholar
  3. Fitzmaurice, G. M., Laird, N. M., & Ware, J. H. (2004). Applied longitudinal analysis. Hoboken: Wiley.Google Scholar
  4. Leyland, A. H., & Goldstein, H. (Eds.). (2001). Multilevel modelling of health statistics. Chichester: Wiley.Google Scholar
  5. Sullivan, L. M., Dukes, K. A., & Losina, E. (1999). Tutorial in biostatistics: An introduction to hierarchical linear modeling. Statistics in Medicine, 18, 855–888.  https://doi.org/10.1002/(SICI)1097-0258(19990415)18:7<855::AID-SIM117<3.0.CO;2-7.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2018

Authors and Affiliations

  1. 1.Department of Biostatistics, School of Public HealthThe University of TokyoBunkyo-kuJapan

Section editors and affiliations

  • Kazuhiro Yoshiuchi
    • 1
  1. 1.The University of TokyoDepartment of Stress Sciences & Psychosomatic Medicine, Graduate School of MedicineBunkyo-kuJapan