Sangam: A Confluence of Knowledge Streams

A baseline admissions prediction model with textual analysis and confidence interval estimations.

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dc.contributor Pham, Van Hoang.
dc.contributor Economics.
dc.contributor Baylor University. Dept. of Economics.
dc.creator Beckham, Stephen Ryan.
dc.date 2014-09-05T13:17:37Z
dc.date 2014-09-05T13:17:37Z
dc.date 2014-08
dc.date 2014-09-05
dc.date.accessioned 2022-05-18T12:10:13Z
dc.date.available 2022-05-18T12:10:13Z
dc.identifier http://hdl.handle.net/2104/9141
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/29208
dc.description Essays submitted out individuals applying to Baylor University may contain hidden information that would assist the admissions department in their decision to accept the appicant. Through textual analysis, this paper attempted to reveal signals of a student's intent to attend Baylor if accepted as well as offering an additional platform to judge a student's ability. Both results are independent of other information gathered from the application process. The models created were found not to be strong enough to act as a stand-alone decision rule. However, the new variables created can be used in Baylor's admission model to increase its effectiveness. The groups of words in commitment, Baylor and admissions groups all prove to be influential, which can be used in other parts of the enrollment process, such as phone interviews. This paper also simulates a confidence range around yield estimates generated from the current model being used at Baylor.
dc.description M.S.Eco.
dc.format application/pdf
dc.format application/pdf
dc.language en_US
dc.publisher
dc.rights Baylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission.
dc.rights Worldwide access
dc.subject Admissions predictions with textual analysis.
dc.subject Ability prediction with textual analysis.
dc.subject Confidence interval estimation with bootstrapping.
dc.subject College admissions.
dc.title A baseline admissions prediction model with textual analysis and confidence interval estimations.
dc.type Thesis


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