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dc.contributor.advisorSmith, Martha K., 1944-en
dc.contributor.advisorLuecke, John Edwinen
dc.creatorKaplan, Andrea Jeanen
dc.date.accessioned2011-02-21T20:20:11Zen
dc.date.accessioned2011-02-21T20:20:16Zen
dc.date.available2011-02-21T20:20:11Zen
dc.date.available2011-02-21T20:20:16Zen
dc.date.issued2010-12en
dc.date.submittedDecember 2010en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2010-12-2462en
dc.descriptiontexten
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.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.subjectMultilevel regressionen
dc.subjectHierarchical linear modelen
dc.subjectMultilevel modelsen
dc.subjectOrdinary Least Squaresen
dc.subjectOrdinary Least Squares regressionen
dc.subjectRegressionen
dc.subjectVariable intercept modelen
dc.subjectVariable slope modelen
dc.titleAn overview of multilevel regressionen
dc.date.updated2011-02-21T20:20:16Zen
dc.description.departmentMathematicsen
dc.type.genrethesisen
thesis.degree.departmentMathematicsen
thesis.degree.disciplineMathematicsen
thesis.degree.grantorUniversity of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Artsen


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