An overview of multilevel regression

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dc.contributor.advisor Smith, Martha K., 1944-
dc.contributor.advisor Luecke, John Edwin
dc.creator Kaplan, Andrea Jean 2011-02-21T20:20:11Z 2011-02-21T20:20:16Z 2011-02-21T20:20:11Z 2011-02-21T20:20:16Z 2010-12 2011-02-21 December 2010
dc.description.abstract Due to the inherently hierarchical nature of many natural phenomena, data collected rests in nested entities. As an example, students are nested in schools, school are nested in districts, districts are nested in counties, and counties are nested within states. Multilevel models provide a statistical framework for investigating and drawing conclusions regarding the influence of factors at differing hierarchical levels of analysis. The work in this paper serves as an introduction to multilevel models and their comparison to Ordinary Least Squares (OLS) regression. We overview three basic model structures: variable intercept model, variable slope model, and hierarchical linear model and illustrate each model with an example of student data. Then, we contrast the three multilevel models with the OLS model and present a method for producing confidence intervals for the regression coefficients.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subject Multilevel regression
dc.subject Hierarchical linear model
dc.subject Multilevel models
dc.subject Ordinary Least Squares
dc.subject Ordinary Least Squares regression
dc.subject Regression
dc.subject Variable intercept model
dc.subject Variable slope model
dc.title An overview of multilevel regression 2011-02-21T20:20:16Z
dc.description.department Mathematics
dc.type.genre thesis
dc.type.material text Mathematics Mathematics University of Texas at Austin Masters Master of Arts

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