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An introduction to hierarchical linear modeling

Open access
Woltman, Heather, Feldstain, Andrea, MacKay, J. Christine, Rocchi, Meredith·2012·Tutorials in Quantitative Methods for Psychology

Abstract

This tutorial aims to introduce Hierarchical Linear Modeling (HLM). A simple explanation of HLM is provided that describes when to use this statistical technique and identifies key factors to consider before conducting this analysis. The first section of the tutorial defines HLM, clarifies its purpose, and states its advantages. The second section explains the mathematical theory, equations, and conditions underlying HLM. HLM hypothesis testing is performed in the third section. Finally, the fourth section provides a practical example of running HLM, with which readers can follow along. Throughout this tutorial, emphasis is placed on providing a straightforward overview of the basic principles of HLM.

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Crossref · 10.20982/tqmp.08.1.p052

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Metadata

Authors
Woltman, H., Feldstain, A., MacKay, J. C., & Rocchi, M.
Venue
Tutorials in Quantitative Methods for Psychology
Published
2012-02-01
Volume
8
Issue
1
Pages
52-69
ISSN
1913-4126 (ISSN-L)
OpenAlex
W2317107098
Cited by
768
References
22
FWCI
26.97
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: An introduction to hierarchical linear modeling. https://app.cochat.ai/papers/10.20982/tqmp.08.1.p052
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Woltman, H., Feldstain, A., MacKay, J. C., & Rocchi, M. (2012). An introduction to hierarchical linear modeling. Tutorials in Quantitative Methods for Psychology. https://doi.org/10.20982/tqmp.08.1.p052

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.