estimating new freshmen enrollment
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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. - PowerPoint PPT PresentationTRANSCRIPT
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.
• Estimate enrollment based on deposits received.
• Make offers to the wait list if needed.
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
Previous Method-Results
Fall Semester Admits
Prev. Years
Yld. RateEst.
EnrollmentAct.
Enrollment
Est. Enr.-
Act. Enr1994 6384 27.15% 1734 1781 -471995 6496 27.90% 1812 1772 401996 6497 27.28% 1772 1739 331997 6633 26.77% 1775 1798 -231998 7004 27.11% 1899 1909 -101999 6765 27.26% 1844 1943 -992000 6761 28.72% 1942 1834 1082001 7787 27.13% 2112 2226 -1142002 7479 28.59% 2138 1899 239
Yield by SAT Score-Fall 2002
SAT Score Admits Enrolled Yield
LE 1150 1433 543 37.89%
1160-1230 1623 481 29.64%
1240- 1280 1321 356 26.95%
1290-1360 1648 325 19.72%
GE 1370 1454 194 13.34%
Total 7479 1899 25.39%
Logistic Regression
• Dichotomous dependent variable.
• Estimates conditional probability of enrollment controlling for multiple independent variables-yield.
• Available in most statistical packages.
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%.
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.
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.
Step One-Baseline Model
Year Offers Est. Enr. Act. Enr. Abs. Diff.
1998 7004 1920 1909 11
1999 6765 1854 1943 89
2000 6761 1853 1834 19
2001 7787 2134 2226 92
2002 7479 2049 1899 151
Total 361
Step Two-Add SAT and HS Avg. 1
Variable Est. Coeff.
Std. Dev Chi Sqr. Pr. > Chi Sqr.
Intercept 8.730 0.295 878.335 0.001
SAT -0.003 0.000 1157.089 0.001
HS Avg. -0.061 0.003 322.130 0.001
Step Two-Add SAT and HS Avg. 2
Year Offers Est. Enr.
Est.
Yield
Act. Enr.
Act.
Yield
1998 7004 1925 27.48% 1909 27.26%
1999 6765 1890 27.94% 1943 28.72%
2000 6761 1854 27.42% 1834 27.13%
2001 7787 2171 27.88% 2226 28.59%
2002 7479 1971 26.36% 1899 25.39%
Step Two-Add SAT and HS Avg. 3
Year Offers Est. Enr. Act. Enr. Abs. Diff.
1998 7004 1925 1909 16
1999 6765 1890 1943 53
2000 6761 1854 1834 20
2001 7787 2171 2226 55
2002 7479 1971 1899 72
Total 216
Full Model 1-Academics
VariableEstimated
CoefficientStandard
ErrorChi-
Square Pr > Chi SqIntercept: enr=1 10.798 0.319 1146.18 <.0001SAT Score -0.004 0.000 1454.38 <.0001HS Avg. -0.071 0.004 403.62 <.0001Missing SAT 0.172 0.092 3.53 0.0603Missing HS Avg. -0.089 0.053 2.87 0.0905
Full Model 2-Inqs/Demo
VariableEstimated
CoefficientStandard
ErrorChi-
Square Pr > Chi SqCollege Day/Night 0.204 0.035 33.45 <.0001HS Visit 0.333 0.104 10.18 0.0014Out of State -0.456 0.052 77.64 <.0001Female -0.229 0.027 72.88 <.0001Blacks--Non Hisp. -0.805 0.060 181.85 <.0001
Hispanic -0.817 0.056 216.27 <.0001
Full Model 3-Inst.
VariableEstimated
CoefficientStandard
ErrorChi-
Square Pr > Chi SqSchool of Mgmt. 0.299 0.041 52.90 <.0001School of Human Devel.
0.572 0.115 24.59 <.0001
School of Nursing 0.259 0.101 6.62 0.0101Engineering -0.155 0.050 9.55 0.002Computer Science 0.239 0.058 16.85 <.0001
Full Model Performance
Year Pred.
Enr
Low
95%
High 95%
Act. Enr. -
Admits
Pred.
Error
1998 1880 1807 1952 1909 29
1999 1919 1846 1991 1943 24
2000 1861 1789 1933 1834 27
2001 2191 2113 2268 2226 35
2002 1961 1886 2036 1899 62
Total 177
Full Model Evaluation
Year Pred.
Enr
Low
95%
High 95%
Act. Enr.
Diff.
1998 1872 1799 1944 1909 37
1999 1910 1837 1983 1943 33
2000 1870 1798 1942 1834 36
2001 2183 2104 2260 2226 43
2002 1974 1900 2049 1899 75
Total 221
Estimating Quality of Regular Admits Fall 2002Estimated Actual Prediction
Error
Mean SAT Score
1231 1238 -7
Mean HS Average
92 92 0
Additional Applications
• Predict retention.
• Identify “Hot Prospects”.
• Identify potential donors.
• Evaluate recruitment efforts.
Logistic RegressionBerge, 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
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