The Institutional Research Office occasionally publishes brief data-based research reports on topics related to various aspects of institutional functioning. Some of these research briefs are listed below, with an executive summary of key findings. 

GRADE DISTRIBUTION - FALL 2019 AND FALL 2020: A comparison of final (not verified) grades for fall 2019 and fall 2020 indicated a slight decrease in the percentage of A and B grades from fall 2019 to fall 2020, especially in graduate courses, and slight increase in the percentage of DFW grades, especially in undergraduate courses. There was a marked increase in the number of incompletes in graduate courses. These very preliminary results suggest that the COVID-19 pandemic did not have a marked effect on students’ academic performance, although the stress associated with the pandemic may have contributed to an increase in course incompletions.

FALL TO SPRING RETENTION RATES FOR FALL 2020 COHORT: The fall-to-spring retention rate for the fall 2020 cohort of incoming freshman was 88.4%, a slight increase over the fall 2019 rate of 87.9% and consistent with the average retention rate of 88.4% for the last five cohorts. However, there were significant gaps of seven to eight percentage points in retention rates for at-risk groups, including under-represented minority students, Pell recipients, and first-generation students. Student athletes and HEOP students, small but important cohorts, showed very strong retention rates for the fall 2020 cohort.

STANDARDIZED TEST SCORES AND STUDENT RETENTION: A preliminary analysis was conducted examining the relationship between standardized test scores (e.g. SAT or ACT) and fall-to-fall retention for new freshmen. Results suggest that for non-health majors, high school GPA is the best predictor of retention. Test scores do not add significantly to the predictive value of high school GPA alone. However, for health majors, while high school GPA is the strongest predictor of retention, test scores do appear to add some predictive value to the model.