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DEVELOPING A MODEL TO EXPLAIN IPEDS GRADUATION

RATES

AT MINNESOTA PUBLIC TWO-YEAR COLLEGES

AND FOUR-YEAR UNIVERSITIES

USING DATA MINING

For more information contact:

Brenda Bailey Ed.D.Associate Director for Research

Minnesota State Colleges and Universities

Brenda.bailey@so.mnscu.edu

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Minnesota State Colleges and UniversitiesCampus Locations

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Background of the Problem

• All postsecondary institutions are required to submit the IPEDS Graduation Rate Survey and disclose graduation rates for Student Right-to-Know

• Reporting graduation rates without reporting supplementary information should be questioned (Astin, 1996)

• Little is known about using IPEDS

data to produce supplementary information about graduation rates at both 2-year and 4-year institutions

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Research Questions

1. What is the relationship between IPEDS graduation rates and institutional characteristics?

2. Given these relationships, what are the predicted graduation rates?

3. How do predicted graduation rates compare to actual graduation rates at Minnesota State system institutions?

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Significance

• Done at institution level

• Predicted graduation rates can provide context

• Little prior research of 2-year college IPEDS data

• No current research uses data mining on both 2-year and 4-year graduation rates

• Identified new predictor variables

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“Data mining is the process of discovering

hidden messages, patterns and knowledge within large amounts of

data and making predictions for outcomes or behaviors” (Luan, p.

17).

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TRADITIONAL STATISTICAL APPROACH: Deductive

Hypothesis

Observation

Confirmation

DATA MINING APPROACH: Inductive

Observation

Pattern

TentativeHypothesis

Theory

Theory

(Trochim, 2002)

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Fall Collection

Winter Collection

Spring Collection

Institutional Characteristics

Survey

Completions Survey

Employees by Assigned

Position Survey

Faculty Salaries Survey

Fall Staff

Survey

Enrollment Survey

Finance Survey

Student Financial

Aid Survey

IPEDS Peer

Analysis System

Graduation Rates Survey

Data Source:IPEDS Data Collection System

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IPEDS Peer Analysis System

Step 1Download IPEDS Data

Microsoft Excel Files

Step 2Build Data Mining Files

Microsoft Access and SPSS Software

Step 3Data Mining C&RT

Clementine Software

Weighted Predicted IPEDS Graduation Rates

Microsoft Access

Flow Chart of Data Analysis

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Algorithm Classification and Regression Tree

(C&RT)• Tree-based classification and prediction method with

binary splits

• Examines input fields and splits records into peer groups with similar output field values

• Graduation rate was set as the output variable

• All other IPEDS variables were set as input fields

• Variables can be nominal or ordinal (categorical) or interval (scale)

• Predicted graduation rate is the average graduation rate for each peer group

• The researcher also calculated a weighted predicted graduation rate for the institutions in each peer group.

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Model Count

Pearson Correlation

r

1 Private for-profit four-year 211 0.885

2 Public four-year 586 0.877

3 Public two-year and less 1,421 0.854

4 Private not-for-profit less than 2-year 114 0.846

5 Private not-for-profit two-year only 221 0.817

6 Private not-for-profit four-year 1,273 0.754

7 Private for-profit two-year only 722 0.751

8 Private for-profit less than two-year 1,223 0.672

Total 5,771

Strong Relationship Between Actual and Predicted Graduation Rate

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Survey Count

Enrollment 22

Institutional Characteristics 19

Student Financial Aid 2

Graduation Rate 2

Salaries 2

NPEC-Salaries 1

Staff 1

NPEC-Finance 1

Completions 1

Total 51

Source of Predictor Variables

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Private For-Profit Four-year Model Predictors

First Split 1. Percent of enrollment that is men

2. Carnegie Classification Code

3. Enrollment age 20-21 total

4. First-time, degree-seeking enrolled PT women

5. Full-year unduplicated graduate HC Non-Resident Alien

6. Full-year unduplicated undergrad headcount Hispanic

7. Percent of enrollment that is first-time

8. Percent of enrollment that is first-time men

9. Service/maintenance staff men NEW

10. State of institution

11. State of residence when student was first admitted

12. Total Awards: Computer and Information Sciences NEW

13. Total completers within 150% of normal time

14. Tuition plan restricted NEW

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First Split 1. Percent of full-time enrollment that is White

2. Average faculty salary male NEW

3. Average faculty salary professor male NEW

4. Enrollment American Indian

5. First-time, degree-seeking enrolled part-time men

6. Full-time enrollment women

7. Full-time retention rate

8. Full-year unduplicated headcount women

9. % of scholarship expenditures from Pell grants

10. % of first-time degree-seeking students submitting SAT

11. % receiving institutional grant aid

12. SAT 1 Math 75th percentile score

13. State of institution

14. Total completers within 150% of normal time

15. Total dormitory capacity

Public Four-year Model Predictors

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First Split 1. Highest Degree offered

2. Adjusted cohort

3. Enrollment age 18-19 women

4. Enrollment age 20-21 women

5. Enrollment age 22-24 men

6. Total completers within 150% of normal time

Public Two-year and Less Model Predictors

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Private Not-for-Profit Less than 2-year Model Predictors

First Split 1. Regional accrediting agency NEW

2. Books and supplies in largest program NEW

3. CIP Code of largest program NEW

4. Degree of urbanization

5. Full year undergraduate White enrollment

6. Full-time Black enrollment

7. Offers programs not leading to a formal award NEW

8. State of institution

9. Total completers within 150% of normal time

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Private Not-for-Profit Two-year Only Model Predictors

First Split 1. 12-month instructional activity credit hours: undergrad

2. Average amount of institutional grant aid received

3. Calendar system NEW

4. Current year GRS cohort as a % of entering class NEW

5. Full year undergraduate White enrollment

6. None of the special learning opportunities are offered NEW

7. Off campus not with family other expenses NEW

8. Off campus with family other expenses NEW

9. Percent of full-time enrollment that is men

10. Percent of undergraduate enrollment that is Black

11. State of institution

12. Total completers within 150% of normal time

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Private Not-for-Profit Four-year Model Predictors

First Split 1. Carnegie Classification Code

2. Adjusted cohort

3. Average faculty salary total NEW

4. Full-time retention rate

5. Name of Regional accrediting agency NEW

6. SAT I Math 25th percentile score

7. Total completed within 150% of time

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Private For-Profit Two-year Only Model Predictors

First Split 1. Total completers within 150% of normal time

2. Adjusted cohort3. Full-year undergraduate total enrollment4. State abbreviation code of institution

Private For-Profit Less than Two-year Model Predictors

First Split 1. Total completed within 150% of normal time

2. Adjusted cohort

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Minnesota State System Four-yearPredictor Variables Differ by Group

Group Predictor Variables

8 % White Completers Full-time Women Room Capacity % Pell Expenditures

12 % White Completers Full-time Women Room Capacity % Submitting SAT

5 % White Completers % with Grant Aid Unduplicated Headcount Women American Indian Enrollment

3 % White Completers % with Institutional Grant Aid State

11 % White Male Faculty Professor Salary Male Faculty SalaryRetention State

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Minnesota State SystemTwo-yearPredictor Variables Differ by Group

Group Predictor Variables

8 Highest Degree Women 18-19 Completers Women 20-21

3 Highest Degree Women 18-19 Completers Women 20-21 Completers

6 Highest Degree Women 18-19 Completers Women 20-21 Completers

1 Highest Degree Women 18-19 Completers Cohort

7 Highest Degree Women 18-19 Completers Cohort

11 Highest Degree Women 18-19 Completers Cohort

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Models Compared to Current Methodology

Model Count

Pearson Correlation

r Relationship

2 Public four-year 586 0.877 Strong

Current Minnesota State system four-year method 586 0.603 Medium

3 Public two-year and less 1,421 0.854 Strong

Current Minnesota State system two-year method 1,421 0.675 Strong

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Some New Predictors

• Average male faculty salary

• Number of awards in Computer Science

• Number of service/maintenance men

• Regional accrediting agency

• No special learning opportunities offered

• CIP code of largest program

• Cost of books and supplies in largest program

• Calendar system• Other expenses off

campus• GRS cohort as a

percent of entering class

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So What at Minnesota State System?

• Could provide national context for Student-Right-to-Know Disclosure forms

• Could provide national context for graduation rate reports and accountability measures

• Identifies peers groups for Minnesota State system colleges and universities

• Shows different predictors for different sectors and peer groups within the system

• Data mining techniques could be used for other system research projects