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  • Title: Creating an evidence-based admissions formula for a new dental school: University of Nevada, Las Vegas, School of Dental Medicine.
    Author: Kingsley K, Sewell J, Ditmyer M, O'Malley S, Galbraith GM.
    Journal: J Dent Educ; 2007 Apr; 71(4):492-500. PubMed ID: 17468310.
    Abstract:
    This article reports development of an evidence-based admissions formula that effectively incorporates the admissions criteria most likely to influence dental school performance. This study utilized peer-reviewed literature and analysis of admissions and performance data from the first three classes of students at the University of Nevada, Las Vegas, School of Dental Medicine (UNLV-SDM). We used Pearson's correlation, linear regression, and ANOVA to determine the strength and direction of association between admissions variables, both singly and in combination, and performance measures. Our initial results revealed no significant relationship between our previous admissions formula, which was adapted from other dental admissions offices, and student performance for our first class and National Board Dental Examination Part I (NBDE-I) (R=.288) or dental school grade point average (DS-GPA) (R=0.193). After using the combined data from the first three classes of students at UNLV-SDM, we confirmed no significant relationship between our previous admissions formula and DS-GPA (R=0.207) and a slight increase in correlation to NBDE-I (R=0.303). More detailed analysis of the admissions variables within the formula revealed that some Dental Admission Test scores, such as Reading Comprehension, Quantitative Analysis, and Biology, were significantly correlated with dental school performance at UNLV-SDM, allowing for revision of the admissions formula to a formula score that is now significantly correlated with student performance for the first class to NBDE-I (R=0.458) and DS-GPA (R=0.368), as well as the combined data from the first three cohorts at UNLV-SDM (R=0.361, 0.218, respectively). In addition, this reformulation did not significantly impact the overall ranking of females or minorities. Although formulaic data can never perfectly predict student performance, this study demonstrated that constant review and revision of relevant admissions criteria are needed for each school to maintain an evidence-based admissions program that provides for fair and effective comparison of student admissions data.
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