1. Estimating New Freshmen Enrollment Agatha Awuah, Eric Kimmelman, Michael Dillon Office of Institutional Research Binghamton University AIRPO June 11-13, 2003

2. Admissions Process Set new freshmen targets. Make offers of admission. Build wait list. Collect deposits. Estimate enrollment based on deposits received. Make offers to the wait list if needed.

3. Previous Method Required to estimate enrollment: 1. Yield=last year’s enrollment (1,000) divided by last year’s offers (3,000). Est. Yield=1,000/3,000 =.33 2. Target for current year (2,000). Est. Offers Needed=2,000/.33 =6,000

4. Previous Method-Results

5. Yield by SAT Score-Fall 2002 SAT ScoreAdmitsEnrolledYield LE 1150143354337.89% 1160-1230162348129.64% 1240- 1280132135626.95% 1290-1360164832519.72% GE 1370145419413.34% Total7479189925.39%

6. Logistic Regression Dichotomous dependent variable. Estimates conditional probability of enrollment controlling for multiple independent variables-yield. Available in most statistical packages.

7. The Data Five fall semesters -1998 to 2002. Only matric freshmen admits (35,796) included. Enrollment of admitted applicants: 9,811. Yield rate: (9,811/35,796)*100=27.4%.

8. Steps to Building Model 1 Estimate baseline model using 5 years of data (intercept only), estimate enrollment, then calculate absolute prediction error by semester. Add additional variables and calculate new absolute prediction error.

9. Steps to Building Model 2 Compare prediction errors. If the second prediction error is smaller than the first, keep new variable in the model. If not, drop it from the model. Continue process until smallest possible prediction error is attained. Predict enrollment for each year in the sample with data from other 4 years.

10. Step One-Baseline Model YearOffersEst. Enr.Act. Enr.Abs. Diff. 199870041920190911 199967651854194389 200067611853183419 200177872134222692 2002747920491899151 Total361

11. Step Two-Add SAT and HS Avg. 1 VariableEst. Coeff. Std. DevChi Sqr.Pr. > Chi Sqr. Intercept8.7300.295878.3350.001 SAT-0.0030.0001157.0890.001 HS Avg.-0.0610.003322.1300.001

12. Step Two-Add SAT and HS Avg. 2 YearOffersEst. Enr. Est. Yield Act. Enr. Act. Yield 199870041925 27.48% 1909 27.26% 199967651890 27.94% 1943 28.72% 200067611854 27.42% 1834 27.13% 200177872171 27.88% 2226 28.59% 200274791971 26.36% 1899 25.39%

13. Step Two-Add SAT and HS Avg. 3 YearOffersEst. Enr.Act. Enr.Abs. Diff. 199870041925190916 199967651890194353 200067611854183420 200177872171222655 200274791971189972 Total216

14. Full Model 1-Academics

15. Full Model 2-Inqs/Demo

16. Full Model 3-Inst.

17. Full Model Performance YearPred. Enr Low 95% High 95% Act. Enr. - Admits Pred. Error 1998188018071952190929 1999191918461991194324 2000186117891933183427 2001219121132268222635 2002196118862036189962 Total177

18. Full Model Evaluation YearPred. Enr Low 95% High 95% Act. Enr. Diff. 1998187217991944190937 1999191018371983194333 2000187017981942183436 2001218321042260222643 2002197419002049189975 Total221

19. Estimating Quality of Regular Admits Fall 2002 EstimatedActualPrediction Error Mean SAT Score 12311238-7 Mean HS Average 92 0

20. Additional Applications Predict retention. Identify “Hot Prospects”. Identify potential donors. Evaluate recruitment efforts.

21. Logistic Regression Berge, D.A. & Hendel, D.D. (2003, Winter). Using Logistic Regression to Guide Enrollment Management at a Public Regional University. AIR Professional File, 1-11. Thomas, E, Dawes, W. & Reznik, G. (2001, Winter). Using Predictive Modeling to Target Student Recruitment: Theory and Practice. AIR Professional File, 1-8. Aldrich, J.H. & Nelson, F.D. (1984). Linear Probability, Logit and Probit Models. Sage University Papers: Quantitative Applications in the Social Sciences, 07-045. Newbury Park, CA: Sage Publications

22. Estimating New Freshmen Enrollment Agatha Awuah, Eric Kimmelman, Michael Dillon Office of Institutional Research Binghamton University AIRPO June 11-13, 2003 Website: http://buoir.binghamton.edu