Predictive Validity Data Set

Verbal and Quantitative Reasoning GRE scores and percentiles were collected by querying the student database for the appropriate information. Any student records that were missing data such as GRE scores or grade point average were removed from the study before the data were analyzed. The GRE Scores of entering doctoral students from 2007-2012 were collected and analyzed. A total of 528 student records were reviewed. Ninety-six records were removed from the data because of a lack of GRE scores. Thirty-nine of these records belonged to MD/PhD applicants who were not required to take the GRE to be reviewed for admission. Fifty-seven more records were removed because they did not have an admissions committee score in the database. After 2011, the GRE’s scoring system was changed from a scale of 200-800 points per section to 130-170 points per section. As a result, 12 more records were removed because their scores were representative of the new scoring system and therefore were not able to be compared to the older scores based on raw score. After removal of these 96 records from our analyses, a total of 420 student records remained which included students that were currently enrolled, left the doctoral program without a degree, or left the doctoral program with an MS degree. To maintain consistency in the participants, we removed 100 additional records so that our analyses only considered students that had graduated with a doctoral degree. In addition, thirty-nine admissions scores were identified as outliers by statistical analysis software and removed for a final data set of 286 (see Outliers below). Outliers We used the automated ROUT method included in the PRISM software to test the data for the presence of outliers which could skew our data. The false discovery rate for outlier detection (Q) was set to 1%. After removing the 96 students without a GRE score, 432 students were reviewed for the presence of outliers. ROUT detected 39 outliers that were removed before statistical analysis was performed. Sample See detailed description in the Participants section. Linear regression analysis was used to examine potential trends between GRE scores, GRE percentiles, normalized admissions scores or GPA and outcomes between selected student groups. The D’Agostino & Pearson omnibus and Shapiro-Wilk normality tests were used to test for normality regarding outcomes in the sample. The Pearson correlation coefficient was calculated to determine the relationship between GRE scores, GRE percentiles, admissions scores or GPA (undergraduate and graduate) and time to degree. Candidacy exam results were divided into students who either passed or failed the exam. A Mann-Whitney test was then used to test for statistically significant differences between mean GRE scores, percentiles, and undergraduate GPA and candidacy exam results. Other variables were also observed such as gender, race, ethnicity, and citizenship status within the samples. Predictive Metrics. The input variables used in this study were GPA and scores and percentiles of applicants on both the Quantitative and Verbal Reasoning GRE sections. GRE scores and percentiles were examined to normalize variances that could occur between tests. Performance Metrics. The output variables used in the statistical analyses of each data set were either the amount of time it took for each student to earn their doctoral degree, or the student’s candidacy examination result.