Assessing the Potential of Geographic Markets fka The Effectiveness of Distant Recruiting Efforts By Gillian Butler Student Affairs Research & Information University of California, Davis CAIR 2001 Sacramento, CA SARI Report 222

Number of HS Graduates in June 2000: Top Ten CA Counties Source: CA Dept of Education

Number of Hispanic HS Graduates in June 2000: Top Ten CA Counties Source: CA Dept of Education

Number of African American HS Graduates in June 2000: Top Ten CA Counties Source: CA Dept of Education

Percentage of Enrolling Students Who Chose UCD Because They "Wanted To Live Near Home" Source: CIRP Survey of Incoming Freshmen,

Percentage For Whom Being Close to Home is “Very Important", by Ethnicity Source: CIRP Survey of Incoming Freshmen, 1997

Academic Image of UC Davis by Admits' Home Locations

Social Image of UC Davis by Admits' Home Locations

Number of Applications, Admits, and Enrolled Students by Marketing Region, '99 through 2000-'01 (Fall Freshmen from HS)

Number of Applied, Admitted, and Enrolled Hispanic Students by Marketing Region '99 through '01 (Fall Freshmen from HS)

Number of Applied, Admitted, and Enrolled African American Students by Marketing Region '99 through '01 (Fall Freshmen from HS)

Potential Eligibles: Completed the course sequence required for admission to a UC or CSU school

Percentage of Total June 2000 Potentially Eligible by Marketing Region Source: CA Dept of Education

Percentage of Total June 2000 HS Graduates Potentially Eligible by Ethnicity Source: CA Dept of Education

Percentage of Hispanic June 2000 HS Graduates Potentially Eligible by Marketing Region Source: CA Dept of Education

Percentage of African-American June 2000 HS Graduates Potentially Eligible by Marketing Region Source: CA Dept of Education

Cesa & Carnegie “Using Logistic Regression to Predict Which Admits Will Register” “Detailed tables of odds that focus on specific groups... are helpful in planning recruitment strategies for those specific groups.”

Logistic Regression Models Bivariate Logistic Regression Two separate models: one to predict admission, & one to predict enrollment Multiplied probability of admission by probability of enrolling to get probability of applicant enrolling. Deviation coding: Results are comparative, not absolute

Maximum Possible Enrollments Multiplied probability of enrolling by relative number of “Potential Eligibles” to derive Maximum Possible Enrollments

MODEL: All Applicants Probability of Enrolling by Number of Potential Eligibles UCD Local UCD Bay UCIUCLAUCRUCSD Probability of an applicant enrolling from this region vs. from UCD Local Number of Potential Eligibles in this region Maximum possible enrollments

MODEL: Hispanic Applicants Probability of Enrolling by Potential Eligibles UCD Local UCD Bay UUCIUCLAUCRUCSD Probability of an applicant enrolling from this region vs. from UCD Local Number of Potential Eligibles in this region Maximum possible enrollments

MODEL: African-American Applicants Probability of Enrolling by Potential Eligibles UCD Local UCD Bay UCIUCLAUCRUCSD Probability of an applicant enrolling from this region vs. from UCD Local Number of Potential Eligibles in this region Maximum possible enrollments

Conclusions The Bay Area is the strongest market for total applicants Local and Bay Area regions combined have same potential as LA County for Hispanic enrollments Of all southern CA markets, only LA County has strong potential for Hispanic enrollments Bay Area has the strongest potential for African-American enrollments