Yi Cao is a data analyst in Enrollment Management. In the past two years at the Portland State University, Yi has worked on a number of research projects, such as Predictive Analysis of 4-year Retention and Graduation for First-time Freshmen, Mapping Risk Factors for Sophomore Retention, and Tracking Retention of Three International Student Cohorts.
One of the statistical analyses Yi has been engaged in recently is the Admission Yield Modeling Project. This study attempts to find out what are statistically significant variables that contribute to Fall term matriculation for admitted undergraduate students. Variables in the analysis include but are not limited to: student demographics (e.g., residency, student population, major, race and ethnicity, country of citizenship), student application (e.g., application date, application decision), pre-college data (e.g., number of transfer institutions student have ever attended prior to Portland State), financial aid (e.g, expected family contribution, un-met need, scholarship recipient), orientation participation, intent to enroll deposit, distance between student home and Portland State, as well as activities (e.g., College fair, Campus tour, Pathway Admitted Student Program, etc.) students have participated before matriculation.
Preliminary analysis has been conducted on admitted Freshmen of the 201204 cohort. Findings reveal that the top five geographic regions having the highest yield rates are: Oregon, California, Washington, Hawaii and international. In addition, Freshmen participated in in-person orientations were more likely to matriculate than those that went through on-line orientations. Freshmen participated in the Pathway Program were more likely to matriculate than those that did not. Freshmen participated in Campus Tour were more likely to matriculate than those that did not. Freshmen who had email/mail/in-person/phone inquiry were more likely to matriculate than those that did not.
Further analysis will focus on both Freshmen and Transfer students of the 201304 cohort, to identify factors that are predictive of their Fall-term matriculation.