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Enrollment Management Predictive Modeling Simplified Vince Timbers, Penn State University
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Enrollment Management Predictive Modeling Simplified
Vince Timbers, Penn State University
Overview
• Common Enrollment Management Uses
• Basic Principles of Predictive Modeling
• Penn State Predictive Models
What is Predictive Modeling?
• Predicting future behavior of a population based on the past behavior of a similar population
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
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
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
Penn State Projection Models
• Retention Projections
• Accepted Student to Enrollment Projections
• Accepted Student Probability of Enrollment
• Paid Deposit to Enrollment Projections
Retention Projections• Retention
• Enrolled students
• College, semester standing
• Official enrollment data
• Contingency table approach
• Build and Test - Rebuild and Retest
Retention ProjectionsContingency 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
University Park Retention ProjectionsCollege Semester
StandingFall 2010Enrolled
Fall 2010RetainedTo Fall 2011
Retention Rate
Fall 2011Enrolled
Projected 2012Retention
AG 01 176 154 87.50% 172 150.50
AG 02 38 31 81.58% 45 36.71
AG 03 220 209 95.00% 200 190.00
AG 04 170 140 82.35% 197 162.24
AG 05 279 259 92.83% 352 326.77
AG 06 174 131 75.29% 178 134.01
AG 07 230 74 32.17% 255 82.04
AG 08 140 18 12.86% 152 19.54
AG 09 61 8 13.11% 76 9.97
AG 10 25 1 4.00% 19 0.76
AG 11 9 2 22.22% 8 1.78
1,114.32
Retention Projection Results
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%
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%
Accepted Student Enrollment Projections (Contingency Table)
Model Variables
• Semester
• Application Pool
• Residency
• College Group
• Academic Performance
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
Probability of Enrollment (Logistic Regression)
• logit= 0+ 1*X1 + 2*X2…… + k*Xk
InterceptApp Date Out of State
HS GPA Verbal Math Writing Predicted PSU GPA
Age Logit Probability
Variable Coefficient 2.13396 -0.00687 -1.14124 -0.24361 -0.00115 -0.00006 -0.00321 0.26485 0.04767
Value 30 0 3.0 700 700 700 3.0 18
2.13396 -0.2061 0 -0.73083 -0.805 -0.042 -2.247 0.79455 0.85806 -0.244360.439212
2
Probability of Enrollment Results (Logistic Regression)
Probability Range
Accepted Paid Deposit Yield
0 (0 - .049) 2852 112 3.930.1 (.05 - .149) 7096 718 10.120.2 3475 713 20.520.3 2192 662 30.20.4 1620 638 39.380.5 1219 610 50.040.6 1051 608 57.850.7 943 671 71.160.8 789 602 76.30.9 580 486 83.791 88 85 96.59
Probability of Enrollment Results (Logistic Regression)
Probability Range
Accepted Paid Deposit Yield
0 (0 - .049) 2852 112 50.1 (.05 - .149) 7096 718 150.2 3475 713 250.3 2192 662 350.4 1620 638 450.5 1219 610 550.6 1051 608 650.7 943 671 750.8 789 602 850.9 580 486 951 88 85 96.59
Paid Deposit to Enrollment Projections
Model Variables (Contingency Table Approach)
• Semester
• Residency
• Placement test completion
Fall 2012 Paid to Enrollment ResultsAs of 5/15/2012
Without Test Completion in Model
• Deposited 8,415• Projected 7,640• Actual 7,574• Difference +59
With Test Completion In Model
• Deposited 8,415• Projected 7,570• Actual 7,574• Difference -4
Test completion=78%
Paid Deposit to Enrollment ResultsAs of 5/29/2012
Without Test Completion in Model
• Deposited 8,342• Projected 7,625• Actual 7,590• Difference +35
With Test Completion In Model
• Deposited 8,342• Projected 7,486• Actual 7,590• Difference -104
• Test completion=88%
Paid Deposit to Enrollment ResultsAs of 7/31/2012
Without Test Completion in Model
• Deposited 8,098• Projected 7,619• Actual 7,632• Difference -47
With Test Completion In Model
• Deposited 8,098• Projected 7,431• Actual 7,632• Difference -201
• Test completion=96%
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
