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Estimating New Freshmen Enrollment

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.. 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 =.33Target for current year (2,000). Est. Offers Needed=2,000/.33 =6,000.

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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

    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 The baseline model includes the intercept only.The baseline model includes the intercept only.

    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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