Leveraging commonly used admission criteria to identify traditionally overlooked applicants
Universities have long had criteria that must be met in order to be admitted to the institution. The purpose of admissions criteria is to determine students who are well suited to begin their academic journey at the university level. The threshold level that these criteria must meet varies across the university landscape, but most, if not all, universities have some commonality with regard to the actual criteria that they require for consideration. In the course of this study, we were unable to find any significant literature that addressed holistically how the threshold levels, against which applicants are measured, were determined. Therefore, this study’s goal is to determine a statistical framework for which any university can determine these levels – to better identify applicants that are likely to be successful as students. A logistic regression model will be built using an innovative dependent variable, to predict the probability of an applicant accruing a specific number of semester credit hours in a given time period, the specifics of which we will discuss through this study. Because of the predictive accuracy, this model can serve as a framework for predicting the likely success of an applicant as a student.