Hierarchical Linear Modeling (HLM)

Matsuyama, Yutaka

doi:10.1007/978-1-4614-6439-6_407-2

Yutaka Matsuyama³

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Synonyms

Covariance components model; Linear mixed-effects model; Multilevel analysis; Random-coefficient model

Definition

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).

Description

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

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.
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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.
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Fitzmaurice, G. M., Laird, N. M., & Ware, J. H. (2004). Applied longitudinal analysis. Hoboken: Wiley.
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Leyland, A. H., & Goldstein, H. (Eds.). (2001). Multilevel modelling of health statistics. Chichester: Wiley.
Google Scholar
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.
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Authors and Affiliations

Department of Biostatistics, School of Public Health, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
Yutaka Matsuyama

Authors

Yutaka Matsuyama
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Correspondence to Yutaka Matsuyama .

Editor information

Editors and Affiliations

University of Miami School of Medicine, Miami, Florida, USA
Marc Gellman
Campbell University School of Pharmacy, Buies Creek, North Carolina, USA
J. Rick Turner

Section Editor information

The University of Tokyo, Department of Stress Sciences & Psychosomatic Medicine, Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan
Kazuhiro Yoshiuchi

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Matsuyama, Y. (2018). Hierarchical Linear Modeling (HLM). In: Gellman, M., Turner, J. (eds) Encyclopedia of Behavioral Medicine. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6439-6_407-2

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DOI: https://doi.org/10.1007/978-1-4614-6439-6_407-2
Received: 12 February 2016
Accepted: 28 November 2017
Published: 27 January 2018
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-6439-6
Online ISBN: 978-1-4614-6439-6
eBook Packages: Springer Reference MedicineReference Module Medicine

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