An introduction to hierarchical linear modeling

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 purpo...

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Main Authors: Heather Woltman, Andrea Feldstain, J. Christine MacKay, Meredith Rocchi
Format: Article
Language:English
Published: Université d'Ottawa 2012-02-01
Series:Tutorials in Quantitative Methods for Psychology
Subjects:
Online Access:http://www.tqmp.org/Content/vol08-1/p052/p052.pdf
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author Heather Woltman
Andrea Feldstain
J. Christine MacKay
Meredith Rocchi
author_facet Heather Woltman
Andrea Feldstain
J. Christine MacKay
Meredith Rocchi
author_sort Heather Woltman
collection DOAJ
description 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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spelling doaj.art-81637f881ae74650a6b9f8299b32c6902022-12-22T01:35:21ZengUniversité d'OttawaTutorials in Quantitative Methods for Psychology1913-41262012-02-01815269An introduction to hierarchical linear modelingHeather WoltmanAndrea FeldstainJ. Christine MacKayMeredith RocchiThis 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.http://www.tqmp.org/Content/vol08-1/p052/p052.pdfHierarchical Linear ModelsNested DataHypothesis TestingParameter Estimation
spellingShingle Heather Woltman
Andrea Feldstain
J. Christine MacKay
Meredith Rocchi
An introduction to hierarchical linear modeling
Tutorials in Quantitative Methods for Psychology
Hierarchical Linear Models
Nested Data
Hypothesis Testing
Parameter Estimation
title An introduction to hierarchical linear modeling
title_full An introduction to hierarchical linear modeling
title_fullStr An introduction to hierarchical linear modeling
title_full_unstemmed An introduction to hierarchical linear modeling
title_short An introduction to hierarchical linear modeling
title_sort introduction to hierarchical linear modeling
topic Hierarchical Linear Models
Nested Data
Hypothesis Testing
Parameter Estimation
url http://www.tqmp.org/Content/vol08-1/p052/p052.pdf
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