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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