1. Template provided by: “posters4research.com” Academic Performance and Persistence of Undergraduate Students at a Land-Grant Institution: A Statistical Analysis Utilizing Detailed Institutional Data Fran Hermanson, Vicki McCracken, and Diem Nguyen Washington State University INTRODUCTIONGRADUATION PROFILE STATISTICAL MODELS 1. Performance Model Y i is the WSU GPA of student i for the first semester. X i is a vector of explanatory variables including: race/ethnicity, gender, age, residency, HSGPA, SAT/ACT, Pell eligibility, full/part- time status, whether a varsity athlete, affiliation with Greek system or Honors College, simultaneously taking Biology/Chemistry/Math, and if in a STEM (Science, Technology, Engineering, and Math) discipline. 2. Graduation Model Z i is a binary random variable, whose value equals 1 if student i graduated by the 6 th (or 5 th ) year and 0 if not; and P i is the graduation probability for student i. X i is a vector of explanatory variables with additional predictors: first semester GPA (adjusted), transferred credits, and stop-out.  Improving student success in postsecondary education is a key federal, state, and university objective that is inseparable from the focus on increasing student access.  In Washington State, about 59 percent of college students graduate within six years. At Washington State University (WSU), a six-year graduation rate is approximately 68 percent.  These statistics raise concerns about the retention and graduation of college students and the need for improving student success at WSU.  This study uses WSU institutional data to analyze factors associated with academic performance and persistence of WSU students and provide some policy implications for improving the retention and graduation rates.  This study applies statistical techniques to determine factors affecting student performance and graduation at WSU.  OLS regression is used for the first-semester performance model with GPA as the response variable and logistic regression is used for the graduation model with a binary response.  The survival analysis method is used to take into account the longitudinal nature of the student progression and the censoring data problem.  Pre-college and post-enrollment variables, student demographics, and institutional characteristics are examined in both performance and graduation models. KEY FINDINGS  High School GPA is a significant predictor of first semester college GPA and positively affects the probability of graduation (both five and six year).  SAT/ACT is a solid (positive) predictor of first semester WSU GPA but does not significantly impact the probability of graduation.  Varsity Athlete status increases the odds of student graduation, and is linked to higher GPAs.  Federal Pell Grant eligibility is associated with lower WSU GPAs and probabilities of completing the degree, indicating financial constraints negatively impact student success.  Students who take Biology/Chemistry/Math all in the first semester have significantly lower first semester GPAs but graduate at similar rates as their counterparts.  Despite differences in graduation rates by race/ethnic groups, the race/ethnicity variables are not significant in the multiple regression/logistical analyses.  Non-continuous enrollment significantly lowers the probability of graduation.  Washington residents had lower first semester GPAs, but had higher probabilities of graduating. IMPLICATIONS  The empirical results suggest an admission process selecting students based on individual potential for success should consider factors in addition to high school GPA and SAT/ACT.  Retention efforts should identify students based on other risk factors (in addition to high school GPA and SAT), such as financial aid, residency, housing, enrollment status, running-start, etc. METHODOLOGY DATA  Analysis based on institutional data for 2 cohorts of students that entered WSU (Pullman campus) as new freshmen in fall 2002 and fall 2003.  Detail included information about the student prior to enrolling and then at the end of 1 st semester, 2 nd semester, and subsequent semesters until 12 th semester.  Analyzed dataset consisted of 5841 students, of which 1894 are censored (had not graduated by the 12 th semester), accounting for over 32 percent of the 2 cohorts. SELECTED RESULTS