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An Overview of the Logic and Rationale of Hierarchical Linear Models

Journal of Management · 1997 · Vol. 23(6) · pp. 723–744
David A. Hofmann

Abstract

Due to the inherently hierarchical nature of organizations, data collected in organizations consist of nested entities. More specifically, individuals are nested in work groups, work groups are nested in departments, departments are nested in organizations, and organizations are nested in environments. Hierarchical linear models provide a conceptual and statistical mechanism for investigating and drawing conclusions regarding the influence of phenomena at different levels of analysis. This introductory paper: (a) discusses the logic and rationale of hierarchical linear models, (b) presents a conceptual description of the estimation strategy, and (c) using a hypothetical set of research questions, provides an overview of a typical series of multi-level models that might be investigated.

Statistical Methods and ApplicationsComplex Systems and Decision MakingAdvanced Statistical Modeling TechniquesMultilevel modelSet (abstract data type)Computer scienceNested set modelConceptual modelHierarchical database modelLinear modelManagement scienceData miningMachine learning
Citations
1,526
FWCI
12.35
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References
89
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99%
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