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Foundations of Enrollment Research

Ma# S&mpson,  Ph.D.   Director  of  Enrollment  Research  and  Modeling   July  28,  2011  

© Performa Higher Education, LLC 2011 All Rights Reserved. Confidential Material: These materials may not be distributed without the consent of Performa Higher Education, LLC


ree Levels of Institutional Action

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Operational •  Programs, People, and processes –  Right administrative system –  Right people responsible for administrative system –  Right processes to ensure data are accurate

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Strategic •  Mining existing data –  Mapping inquiries, applied, admitted, and enrolled –  Pipelines –  Yield Rates –  Examine multiple points in the funnel •  Inquiry •  Applied •  Admitted •  Enrolled

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Sample Map - Inquired

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Sample Map – Enrolled

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Sample Pipeline Comparison Top 10  An&cipated  Major  interests  for  all   inquiries:   –  Nursing  (2,689)   –  Business  Administra&on  (2,215)   –  Pre  Med/Vet/Dental  (2,128)   –  Other  (1,703)   –  Psychology  (1,423)   –  Communica&on  Arts  (1,384)   –  Educa&on  (1,374)   –  Music  (1,041)   –  Early  Childhood  Educa&on  (1,009)   –  Biology  (987)  

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Top 10  An&cipated  Major  interests  for  all   enrolled  students:   –  Nursing  (213)   –  Early  Childhood  Educa&on  (93)   –  Business  Administra&on  (80)   –  Psychology  (46)   –  Biology  (45)   –  Accoun&ng  (40)   –  Sports  Management  (37)   –  Zoo  Biology  (37)   –  Middle  Childhood  Educa&on  (35)   –  Communica&on  Arts  (34)  


Sample Yield Rate Table

Source Code  

Enrolled

Not Enrolled  

Total

Yield

Name of   source  code  

Total number   of  enrolled   students  with   source  code  

Total number   students  who   did  not  enroll   with  source   code  

Total number   of  students   enrolling  and   not  enroll  with   source  code  

(Enrolled/ Total)*100

NRCCUA

10

1000

1010

0.9%

Visit

150

985

1135

13.22%

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Leverage •  Predictive Modeling •  Admitted Student Research •  Benchmarking

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What is Predictive Modeling •  Normally referrers to some type of regression analysis •  Regression is an attempt to predict an outcome based on one or more variables •  Predicting college GPA based on previous academic performance •  Predicting probabilities of contracting a disease based on a set of health factors •  Predicting probabilities of enrollment based on a set of criteria

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Predictive Modeling •  Allows for a probability of enrollment to be assigned to every student in the inquiry pool •  Students with higher probabilities of enrollment are targeted more strategically –  Helps to streamline communications –  Significant cost savings can be achieved –  Staff are able to efficiently prioritize inquiries –  Output can be difficult to understand –  Requires a fairly complex understanding of statistics to accomplish www.PerformaHE.com


How to Accomplish Predictive Modeling •  •  •  • 

Step 1: Identify variables Step 2: Fit a regression equation Step 3: Test the regression equation Step 4: Apply regression equation to current inquiry pool

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Step 1: Identify Variables •  Consider the variables to be used –  Anticipated Major –  Counselor –  Demographic Information –  Market (in or out of market?) –  Qualities and characteristics of a student s zip code –  Source Code –  Territories

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Probability of Enrollment

Step 2: Fit a regression equation

Independent Variable(s) www.PerformaHE.com Â


Probability of Enrollment

Step 2: Fit a regression equation

Major

Source Code Gender Ethnicity

Independent Variable(s) www.PerformaHE.com Â


Step 2: Fit a regression equation

Probability of Enrollment

Major (2.139)

Source Code (1.985) Intercept (0.989)

Ethnicity (1.012)

Independent Variable(s) www.PerformaHE.com Â

Gender (1.345)


Step 2: Fit a regression equation Ln(p)=0.989+2.139(Major)+1.985(Source Code)+1.345(Gender)+1.012(Ethnicity)

Probability of Enrollment

Major

Source Code Intercept

Gender Ethnicity

Independent Variable(s) www.PerformaHE.com Â


Step 3: Test the Regression Equation Rank

Enrolled

Predicted Enrolled  

Not Enrolled  

Predicted Not   Enrolled  

1

341

394.65

1856

1802.35

2

233

223.78

1955

1964.22

3

122

140.28

2080

2061.72

4

82

98.50

2114

2097.50

5

55

59.65

2139

2134.35

6

35

37.38

2163

2160.62

7

23

28.44

2172

2166.56

8

15

18.59

2183

2179.41

9

9

9.50

2186

2185.50

10

1

2.43

2195

2193.57

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Step 3: Test the Regression Equation Rank

% Enrolled  

% Not   Enrolled  

% Total   Enrolled  

Cum %  of   Enrolled  

1

15.5

84.5

37.2

37.2

2

10.6

89.4

25.4

62.6

3

5.5

94.5

13.3

75.9

4

3.7

96.3

9.0

84.9

5

2.5

97.5

6.0

90.9

6

1.6

98.4

3.8

94.7

7

1.0

99.0

2.5

97.2

8

0.7

99.3

1.6

98.8

9

0.4

99.6

1.2

100.0

10

0.0

100.0

0.0

100.0

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Step 4: Apply Regression Equation to Current Inquiry Pool 1 Probability of Enrollment in Log Odds

Sue Johnston, p = 89%

0

-1

Patrick Stuart, p = 45%

Bill Paul, p = 5% All Predictor Variables

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Using a Model •  Predicted probabilities provide a path of engagement priority •  Engage the students most likely to enroll with the most expensive and resource heavy methods •  Order students by –  Actual probabilities –  Decile ranks –  Percentile ranks

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Benefits •  Cost savings –  Significant savings can be achieved by modifying communication flow based on predictive model

•  Prioritize –  Counselor time –  Telecounseling –  Travel

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Foundations of Enrollment Research  

enrollment research, predictive modeling, tim fuller, admission

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