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 CollegeSemester Standing Fall 2010 Enrolled Fall 2010 Retained To Fall 2011 Retention Rate Fall 2011 Enrolled Projected 2012 Retention AG0117615487.50%172150.50 AG02383181.58%4536.71 AG0322020995.00%200190.00 AG0417014082.35%197162.24 AG0527925992.83%352326.77 AG0617413175.29%178134.01 AG072307432.17%25582.04 AG081401812.86%15219.54 AG0961813.11%769.97 AG102514.00%190.76 AG119222.22%81.78 1,114.32

11. Retention Projection Results University Park Retention 2011 Projection 24,662 2011 Actual 24,761 Under Projected.5% 2012 Projection24,851 2012 Actual25,046 Under Projected.8% Change of Campus to University Park 2011 Projection3,617 2011 Actual3,540 Over Projected 2.1% 2012 Projection3,459 2012 Actual3,380 Over Projected 2.3%

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) InterceptApp DateOut of State HS GPAVerbalMathWritingPredicted PSU GPA AgeLogitProbability Variable Coefficient 2.13396-0.00687-1.14124-0.24361-0.00115-0.00006-0.003210.264850.04767 Value 3003.0700 3.018 2.13396-0.20610-0.73083-0.805-0.042-2.2470.794550.85806-0.244360.4392122

15. Probability of Enrollment Results ( Logistic Regression) Probability Range AcceptedPaid DepositYield 0 (0 -.049) 28521123.93 0.1 (.05 -.149) 709671810.12 0.2347571320.52 0.3219266230.2 0.4162063839.38 0.5121961050.04 0.6105160857.85 0.794367171.16 0.878960276.3 0.958048683.79 1888596.59

16. Probability of Enrollment Results ( Logistic Regression) Probability Range AcceptedPaid DepositYield 0 (0 -.049) 28521125 0.1 (.05 -.149) 709671815 0.2347571325 0.3219266235 0.4162063845 0.5121961055 0.6105160865 0.794367175 0.878960285 0.958048695 1888596.59

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

18. Fall 2012 Paid to Enrollment Results As of 5/15/2012 Without Test Completion in Model Deposited8,415 Projected7,640 Actual7,574 Difference+59 With Test Completion In Model Deposited 8,415 Projected7,570 Actual7,574 Difference-4 Test completion=78%

19. Paid Deposit to Enrollment Results As of 5/29/2012 Without Test Completion in Model Deposited8,342 Projected7,625 Actual7,590 Difference+35 With Test Completion In Model Deposited8,342 Projected7,486 Actual7,590 Difference-104 Test completion=88%

20. Paid Deposit to Enrollment Results As of 7/31/2012 Without Test Completion in Model Deposited 8,098 Projected7,619 Actual7,632 Difference-47 With Test Completion In Model Deposited8,098 Projected7,431 Actual7,632 Difference-201 Test completion=96%

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