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dc.contributor.authorPratt, Megan Pageen_US
dc.date.accessioned2011-08-06T16:01:33Z
dc.date.available2011-08-06T16:01:33Z
dc.date.issued2004-05-07en_US
dc.identifier.otheretd-05192004-133719en_US
dc.identifier.urihttp://hdl.handle.net/10919/9933
dc.description.abstractOver the past two decades, a surge of interest in the area of forecasting has produced a number of statistical models available for predicting the winners of U.S. presidential elections. While historically the domain of individuals outside the scholarly community - such as political strategists, pollsters, and journalists - presidential election forecasting has become increasingly mainstream, as a number of prominent political scientists entered the forecasting arena. With the goal of making accurate predictions well in advance of the November election, these forecasters examine several important election "fundamentals" previously shown to impact national election outcomes. In general, most models employ some measure of presidential popularity as well as a variety of indicators assessing the economic conditions prior to the election. Advancing beyond the traditional, non-scientific approaches employed by prognosticators, politicos, and pundits, today's scientific models rely on decades of voting behavior research and sophisticated statistical techniques in making accurate point estimates of the incumbent's or his party's percentage of the popular two-party vote. As the latest evolution in presidential forecasting, these models represent the most accurate and reliable method of predicting elections to date. This thesis provides an assessment of forecasting models' underlying epistemological assumptions, theoretical foundations, and methodological approaches. Additionally, this study addresses forecasting's implications for related bodies of literature, particularly its impact on studies of campaign effects.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.relation.haspartThesis.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectpresidential electionsen_US
dc.subjectforecasting modelsen_US
dc.subjectpredictionen_US
dc.subjectcampaign effectsen_US
dc.titlePredicting Presidential Elections: An Evaluation of Forecastingen_US
dc.typeThesisen_US
dc.contributor.departmentPolitical Scienceen_US
dc.description.degreeMAen_US
thesis.degree.nameMaster of Artsen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
dc.contributor.committeechairShingles, Richard D.en_US
dc.contributor.committeememberBrians, Craig Leonarden_US
dc.contributor.committeememberHult, Karen M.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05192004-133719en_US
dc.date.sdate2004-05-19en_US
dc.date.rdate2004-05-25
dc.date.adate2004-05-25en_US


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