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dc.creatorKaplan, Andrea Jean
dc.date.accessioned2011-02-21T20:20:11Z
dc.date.accessioned2011-02-21T20:20:16Z
dc.date.available2011-02-21T20:20:11Z
dc.date.available2011-02-21T20:20:16Z
dc.date.created2010-12
dc.date.issued2011-02-21
dc.date.submittedDecember 2010
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2010-12-2462
dc.descriptiontext
dc.description.abstractDue 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.mimetypeapplication/pdf
dc.language.isoeng
dc.subjectMultilevel regression
dc.subjectHierarchical linear model
dc.subjectMultilevel models
dc.subjectOrdinary Least Squares
dc.subjectOrdinary Least Squares regression
dc.subjectRegression
dc.subjectVariable intercept model
dc.subjectVariable slope model
dc.titleAn overview of multilevel regression
dc.date.updated2011-02-21T20:20:16Z
dc.description.departmentMathematics
dc.type.genrethesis*
thesis.degree.departmentMathematics
thesis.degree.disciplineMathematics
thesis.degree.grantorUniversity of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Arts


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