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An introduction to hierarchical linear modeling

Bibliographic Data

ID23345980
AuthorsHeather Woltman (University of Ottawa), Andrea Feldstain (0000-0001-5284-7543), Jennifer Christine MacKay, Meredith Rocchi (0000-0002-5851-1693)
Year2012
Volume8
Issue1
Pages52-69
Publication date2012-02-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueTutorials in Quantitative Methods for Psychology (JOURNAL)
Journal identifiersISSN: 1913-4126 • E-ISSN: 1913-4126
PublisherThe Quantitative Methods for Psychology (PUBLISHER)
DOI10.20982/tqmp.08.1.p052
OpenAlexW2317107098
LanguageEN
Citations received144
References cited16

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.

Advanced Statistical Methods and Models · Computer Science · Statistical Methods and Applications · Statistical Methods and Bayesian Inference

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Unique citing works144
Citations per year12
Citation span2014 - 2026 (13)
Citation velocitycurrent
Highly citedYes
Citation typesNeutral: 137

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