Enrollment Management Predictive Modeling Simplified

1. Enrollment Management Predictive Modeling Simplified Vince Timbers, Penn State University

2. Overview • Common Enrollment Management Uses • Basic Principles of Predictive Modeling • Penn State Predictive Models

3. What is Predictive Modeling? • Predicting future behavior of a population based on the past behavior of a similar population

4. Common Uses of Predictive Modeling in Enrollment Management • Retention projections • Applicant enrollment projections • Accepted student enrollment projections • Suspect/prospect application projections • Recruitment and retention strategies and activities • Budget and resource planning

5. Predictive Modeling Basics • Past behavior is a good predictor of future behavior • Similar groups tend to behave in a similar manner, under similar circumstances • Model effectiveness depends on the ability to identify similar groups and similar circumstances • Always test new models on historic data

6. Model Building Steps • Identify what is being predicted • Identify the population • Identify predictors • Select data sources • Select a modeling technique • Build and Test - Rebuild and Retest

7. Penn State Projection Models • Retention Projections • Accepted Student to Enrollment Projections • Accepted Student Probability of Enrollment • Paid Deposit to Enrollment Projections

8. Retention Projections • Retention • Enrolled students • College, semester standing • Official enrollment data • Contingency table approach • Build and Test - Rebuild and Retest

9. Retention Projections Contingency Table Approach • Aggregated prior data to the appropriate level • Calculate retention rates • Aggregated current data to the appropriate level • Apply prior retention rates to current data to calculate the retention projection

10. University Park Retention Projections

11. Retention Projection Results Change of Campus to University Park • 2011 Projection 3,617 • 2011 Actual 3,540 • Over Projected 2.1% • 2012 Projection 3,459 • 2012 Actual 3,380 • Over Projected 2.3% University Park Retention • 2011 Projection 24,662 • 2011 Actual 24,761 • Under Projected .5% • 2012 Projection 24,851 • 2012 Actual 25,046 • Under Projected .8%

12. Accepted Student Enrollment Projections (Contingency Table) Model Variables • Semester • Application Pool • Residency • College Group • Academic Performance

13. Accepted Student Probability of Enrollment Logistic Regression • Explain the relationship between a discrete outcome (enrollment) and a set of explanatory variables • Logistic Regression produces a set of coefficients (model) used to predict the outcome (enrollment) for similar populations

14. Probability of Enrollment (Logistic Regression) • logit=0+ 1*X1 + 2*X2…… + k*Xk

15. Probability of Enrollment Results (Logistic Regression)

16. Probability of Enrollment Results (Logistic Regression)

17. Paid Deposit to Enrollment Projections Model Variables (Contingency Table Approach) • Semester • Residency • Placement test completion

18. Fall 2012 Paid to Enrollment ResultsAs of 5/15/2012 Without Test Completion in Model With Test Completion In Model Deposited 8,415 Projected 7,570 Actual 7,574 Difference -4 Test completion=78% • Deposited 8,415 • Projected 7,640 • Actual 7,574 • Difference +59

19. Paid Deposit to Enrollment ResultsAs of 5/29/2012 Without Test Completion in Model With Test Completion In Model Deposited8,342 Projected 7,486 Actual 7,590 Difference -104 Test completion=88% • Deposited 8,342 • Projected 7,625 • Actual 7,590 • Difference +35

20. Paid Deposit to Enrollment ResultsAs of 7/31/2012 Without Test Completion in Model With Test Completion In Model Deposited8,098 Projected 7,431 Actual 7,632 Difference -201 Test completion=96% • Deposited 8,098 • Projected 7,619 • Actual 7,632 • Difference -47

21. Model Building Steps • Identify what is being predicted • Identify the population • Identify predictors • Select data sources • Select a modeling technique • Build and Test - Rebuild and Retest

22. Questions? Thank You! Vince Timbers vlt@psu.edu