1999•The Journal of Experimental EducationRequires access

Reasons for Hierarchical Linear Modeling: A Reminder

Jianjun Wang

Open publisher page 13 citations

Abstract

Delimitations of hierarchical linear modeling (HLM) were examined in terms of fixed and random effects in multilevel data analyses. The author used examples at the local and national levels to illustrate proper applications of HLM and dummy variable regression. Cautions are raised regarding circumstances under which hierarchical data do not need HLM.

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What this paper is about

Delimitations of hierarchical linear modeling (HLM) were examined in terms of fixed and random effects in multilevel data analyses. The author used examples at the local and national levels to illustrate proper applications of HLM and dummy variable regression. Cautions are raised regarding circumstances under which hierarchical data do not need HLM.

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OpenAlex reports 13 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Delimitations of hierarchical linear modeling (HLM) were examined in terms of fixed and random effects in multilevel data analyses. The author used examples at the local and national levels to illustrate proper applications of HLM and dummy variable regression. Cautions are raised regarding circumstances under which hierarchical data do not need HLM.

Key concepts: Multilevel model, Hierarchical database model, Linear regression, Random effects model, Computer science, Multilevel modelling, Regression analysis, Statistics

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