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Access to Success: Leading Indicators Workgroup. The University of Hawaiʻi at Mānoa. New Student Enrollment. Retention Rates. Graduation Rates. Focus of Study. First Time freshmen of Fall 2003 n = 1,809 Enough history to examine first term retention through degree completion

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access to success leading indicators workgroup
Access to Success:Leading Indicators Workgroup

The University of Hawaiʻi at Mānoa

focus of study
Focus of Study
  • First Time freshmen of Fall 2003
    • n = 1,809
    • Enough history to examine first term retention through degree completion
  • Transfer students entering in Fall 2009
    • n = 1,804
    • Currently studying retention
multivariate analysis
Multivariate Analysis

Data Collection, Causal Modeling,

Results

data collection
Data Collection
  • Created a longitudinal database with over 100 data elements theoretically related to retention & graduation(see handout for list of variables):
    • Demographic
    • Geographic Origin
    • Pre-Collegiate Experiences
    • Academic and Course Experiences
    • Campus Experience
    • Financial Aid
    • Interaction Variables
    • Additional Variables from LI Research!
types of variables analyzed
Types of Variables Analyzed

Gender

Age

Ethnicity

Residency

Geographic Origin

On Campus Employment

Housing

Student Life Activities

Athletics

STAR Usage

Average Class Size

Campus Experience

Demographics

High School GPA & Rank

SAT

AP CLEP

Educational Goals

Transfer GPA

# Transfer Credits

Need Based Aid

Non-need Based Aid

Pell Grant

Work Study

% of Aid Met

Retention & Degree Completion

Financial Need

Pre-College

Major

Credit Load

Credits Earned

First Term GPA

Distance Education

Dual Enrollment

High Failure Rate Courses

Courses Taken (including Math & English)

Ethnicity by Geographic Origin

Employment by Housing

High School GPA by First Term GPA

Residency by Need Based Aid

Ratio of Successful Adds to Drops

Interactions

Academic

strongest predictors of degree completion fall 2003 freshmen
Strongest Predictors of Degree Completion(Fall 2003 Freshmen)

Strongest

Credits Earned Yr. 1

These variables account for approximately 34% of the variance in a student’s likelihood of completing a degree (Pseudo R Square = .344).

First Term GPA

145.560 (<.001)*

23.883 (<.001)*

Geographic Origin

23.369 (<.001)*

Degree Completion

Dual Enrollment

21.084 (<.001)*

12.004 (.001)*

Ethnicity

11.816 (.001)*

Enrollment in College Level Math Year 1

6.177 (.013)*

*Wald statistic (sig.)

The Wald test statistic was used to indicate strength of the variable instead of the coefficient, standardized beta. Because of the nature of the logistic regression, the coefficient is not easily interpretable to indicate strength.

High School GPA

Weakest

findings
Findings
  • Variables significant in predicting degree completion of freshmen:
    • “Expected” predictors emerging from model:
      • Ethnicity (Asian students 2x greater odds)
      • Geographic Origin (1.9x greater odds for HI students)
      • First Term GPA (1.5x greater odds per grade point increase)
    • Not-so obvious predictors:
      • >= 24 Credits Earned in Year 1 (Odds Ratio = 6x)
      • Dual Enrollment (Odds Ratio = 2x)
      • Enrollment in College-Level Math in Year 1 (Odds Ratio = 1.5x)
      • Prior Credits Earned (Odds Ratio = 1.5x)
overall model performance
Overall Model Performance
  • 73% of observations correctly classified
    • Sensitivity: 76%
    • Specificity: 70%
strongest predictors of transfer student retention fall 2009 transfers preliminary results
Strongest Predictors of Transfer Student Retention (Fall 2009 Transfers’ Preliminary Results)

Strongest

1st Term GPA

These variables account for approximately 26% of the variance in a student’s likelihood of completing a degree (Pseudo R Square = .258).

Ethnicity

34.019 (<.001)*

26.995 (<.001)*

Declared Major

19.174 (<.001)*

Transfer Student Retention

On Campus Employment

17.094 (<.001)*

8.776 (.003)*

Geographic Origin

7.080 (.008)*

Need-Based Aid

4.010 (.045)*

*Wald statistic (sig.)

The Wald test statistic was used to indicate strength of the variable instead of the coefficient, standardized beta. Because of the nature of the logistic regression, the coefficient is not easily interpretable to indicate strength.

Distance Education

Weakest

what can advisors do
What can advisors do?
  • Engaging the students in understanding
    • Positive Psychology
      • i.e How taking 15 credits will help students graduate in 4 years
      • Showing students the cost implications of delaying their studies
      • The power of a phone call
  • Targeted interventions
    • Use at-risk forecasting data to predict and inform
    • Focus on at-risk students on the “threshold”
good examples
Good Examples
  • Florida State University
    • 85% retention rate in 2000 up to 91% in 2009
    • Retention Task Force started at the very top level
    • Main efforts were in IR and Advising
  • U of Nevada at Reno
    • 76% in 2005 up to 80% in 2010.
    • Retention Task Force started at the very top; same focus as FSU
    • Quantified data in terms of $/revenue.
    • Covered in Media nationally
m noa policy implications
Mānoa Policy Implications
  • Facilitate credit momentum
    • “Do it in 4” Project
    • Students missing credit milestones are called in for advising
  • Drive enrollment in Math & English in students’ first year
    • Automatic (pre)registration for first year students
    • Examine course pressure points; course wait listing
  • Mandatory advising in first semester on campus
    • Mandatory declaration of major by second year on campus
  • New opportunities for on-campus employment:
    • Undergraduate Research, Student Success Fellowships, Legislative Internships
  • Fast track transfer policies
    • Automatic admit for UH System students
    • Transfer programs like “Ka’ie’ie”
who are we
Who are we?

Mānoa Institutional Research Office

www.manoa.hawaii.edu/ovcaa/mir

Dr. Ron Cambra

Assistant Vice Chancellor  for Undergraduate Education808-956-6231

cambra@hawaii.edu

John Stanley

Institutional Analyst

808-956-5366

jstanley@hawaii.edu