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WRITING MATTERS: UTILIZING SAT
WRITING TO PREDICT STUDENT SUCCESS
College Board National Forum 2012 – Miami, Florida 8:45 – 9:45 Thursday, October 25
Session Agenda
Presenter Introductions:
Chris Lucier, Vice President for Enrollment Management, University of Vermont
Jessie Donavan, Senior Enrollment Management Analyst, University of Vermont
Pam Horne, Associate Vice Provost of Enrollment Management & Dean of Admissions, Purdue University
Brent Drake, Director of Enrollment Management Analysis and Reporting, Purdue University
University Profiles
Purdue University Analysis
University of Vermont Analysis
Considerations for Future Research
University of Vermont Profile
Founded in 1791
Traditional residential campus located in Burlington, VT
Public, high research university
Residency mix:
33% VT
67% Out of State
101 UG Programs
Fall 2012 Enrollment:
UG 10, 192
Grad 1,327
Med 446
SAT Ranges:
Critical Reading 560-670
Math 570-670
Writing 560-670
Purdue University Profile
Located in West Lafayette IN
Public, Research Very Intensive institution
30,776 undergraduates, 924 professional, 7,937 graduate students
Traditional residential campus
283 programs of study
Students are admitted directly to major (91% to degree granting major, 9% exploratory)
61% of applicants admitted
Among undergraduates – 58% Indiana, 26% U.S. non-resident, 16% international
SAT Interquartile ranges
Critical Reading 510-620
Math 550-680
Writing 510-620
SAT Validity @ Purdue
National Studies bi-variate correlation to first-year GPA for all
3 parts range from 0.25 to 0.4 (writing usually highest)
When corrected for attenuation range from 0.40 to 0.60
National Studies multiple correlations SAT only 0.3 to 0.5
When combined with other pre-college academic
predictors (HS GPA, AP courses, years of study) range is
0.3 to 0.7
SAT Validity @ Purdue
Bivariate and multiple correlations largely the same as
national studies
Typically Math and Writing show highest bi-variate
relationship to student success measures
Prediction of first-year GPA overall typically within 0.04 of
actual first-year GPA
Results similar with subgroups, greatest difference among
Black/African American students
Multiple Correlations At A Single Institution
Multiple correlations between measures of academic success and unit level student
entering academic profiles at one institution tend to lead to correlations of low to
moderate effect sizes (Bridgeman, McCamley-Jenkins, & Ervin, 2000)
Some reasons identified in the literature (Stumpf & Stanley, 2002)
Restriction of range inherent in any one institutions academic profile
The interdependence of academic profile variables from high school
transcripts and standardized tests
The disparate academic experience of students in one institution
Multiple Correlations At A Single Institution
Stumpf and Stanley (2002) proposed examining grouped data across multiple
institutions to correct for the attenuation of existing relationships
Stumpf and Stanley (2002) utilized group variables of 25th and 75th percentiles
of standardized test scores and the percentage of a class with a high school
G.P.A. >= 3.0 to predict six-year graduation rates.
Purdue Replication
Predictor variables were data items readily available in two national data sets
Integrated Post-Secondary Education Data System (IPEDS)
25th and 75th percentile of SAT Scores
US News and World Report National Colleges data set
Percentage of students in top ten percent of high school class
Criterion variable was six-year graduation rates pulled from IPEDS
Final data set consisted of 199 institutions
Multiple correlation for model R2 = 0.8025
Use national model at university level to predict success among
different subgroups
How well are individual colleges performing versus their
predicted performance
Predictive Modeling @ UVM
We have built an econometric model that predicts a student’s
“success” at UVM
Success is defined as 2nd semester GPA
Historically the model has included high school rank, a high
school quality index and the highest combined SATR+SATA
SAT Writing @ UVM
In the summer of 2011 we analyzed data from classes
entering in Fall of 2009 and Fall of 2010
We found that the SATW score had a higher correlation
with 2nd semester GPA than SATR+SATA
This was the case even in colleges such as engineering
Including both the SATW and SATR+SATA in the model
strengthened the predictive value of the model
Utilizing SAT with Student Record
While there is a strong correlation between SAT
scores and 2nd semester GPA, you cannot build a
strong predictive model with SAT score alone
SAT scores are best used in conjunction with other
factors from a student’s record such as class rank,
high school GPA and high school quality
A student’s SAT scores account for 1/3 of the
predictive value of our admissions model
Other Uses of the SAT at UVM
Honors College Selection Process
Scholarship qualification
Awarding of financial aid need
based funds
Relationship Between SAT Writing
and 2nd Semester GPA
SAT Writing Band Avg. 2nd Semester GPA
350-500 2.73
500-540 2.85
550-590 2.95
600-640 3.07
650-690 3.13
700-740 3.31
750+ 3.31
Advice For Predictive Models
If don’t have the expertise and resources within
your institution use College Board ACES
Doing so also adds to national validity studies
Critically analyze your own models annually or
at least every two years
Statistically
Against your goals
What are you trying to predict and why
Recommendations for SAT Writing
If you require writing scores, use them in admissions and scholarship
selection
If you collect writing scores, report them to stakeholders
Trustees, executive officers
Faculty
State educational offices
K-12 educators
Prospective students
Continue to educate press and others regarding 2400 scale!
Considerations for Future Research
Difference between SAT Writing and Critical Reading among
international students
Other measures of academic success beyond first-year GPA
one- and two-year retention
4- and 6-year graduation
time-to-degree
grades in subsequent writing-intensive courses
Recommended