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Estimating New Freshmen Enrollment

Estimating New Freshmen Enrollment. Agatha Awuah, Eric Kimmelman, Michael Dillon Office of Institutional Research Binghamton University AIRPO June 11-13, 2003. Admissions Process. Set new freshmen targets. Make offers of admission. Build wait list. Collect deposits.

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Estimating New Freshmen Enrollment

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  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: • Yield=last year’s enrollment (1,000) divided by last year’s offers (3,000). Est. Yield=1,000/3,000 =.33 • 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

  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

  11. Step Two-Add SAT and HS Avg. 1

  12. Step Two-Add SAT and HS Avg. 2

  13. Step Two-Add SAT and HS Avg. 3

  14. Full Model 1-Academics

  15. Full Model 2-Inqs/Demo

  16. Full Model 3-Inst.

  17. Full Model Performance

  18. Full Model Evaluation

  19. Estimating Quality of Regular Admits Fall 2002

  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

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