[Important Information for Students] Data Matters September 2025 : Monthly Insights and Updates from Institutional Effectiveness

[SUNY Plattsburgh Office of Institutional Effectiveness]

Welcome to Data Matters, your monthly update from the Office of
Institutional Effectiveness!

Data Spotlight: Strengthen Student Success and Engage the North Country
Region

The Career Development Center helps students synthesize education and
experiences as they prepare for life after college while also working to
engage North Country employers. The below information highlights outcomes
of the Career / Applied Learning Grants in the prior academic year. These
grants help make unpaid and underpaid experiences affordable.

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Upcoming OIE Events

Got Data? Presentations

   -

   Title IX on Campus: Kim Irland | 11 a.m Thursday, October 9 | Alumni
   Conference Room ACC
   -

   Predicting Student Retention, OIE Fellowship Project Results: Dr. Sabah
   Bushaj | 3 p.m. Thursday, November 13 | Hermes Conference Room Au Sable Hall
   -

   Belonging and Self-Efficacy of Males Teacher Candidates: Dr. Katie Alton
   | 1 p.m. Thursday, December 4 | Location TBD


Data Challenge and Last Month’s Results

Last month, we explored final grades and sex. We noted that there were
approximately 1.5 times as many female students as there were male students
in our dataset. Calculating the mean and median GPAs helped us compare each
groups' overall performance in aggregate, noting that although median GPAs
were the same, female students had a slightly higher mean GPA. The boxplots
for each group's distribution looked very similar across outliers and
percentiles. Lastly, our choice of a t-test or chi-square test came down to
whether we were going to use the letter grades (categorical) or numeric
grades (continuous). Since the numeric grades were already created from
earlier analysis, the t-test was appropriate to determine if the
differences between the groups were significant. The resulting p-value was
much less than 0.05, indicating a statistically significant difference
existed.

This month, let’s introduce two terms that are often used interchangeably
in conversation: probability and likelihood. Probability takes a model of
something and predicts outcomes, keeping the parameters of the model fixed.
Likelihood, on the other hand, uses data to estimate the parameters of the
model. Consider coin-flipping where a coin has two sides and each side is
one outcome. We know that if you flip it once, assuming the coin is fair,
the probability of heads is 50%. But now let’s say that you didn’t KNOW the
coin was fair, you could flip the coin 100 times and keep track of each
outcome. If you get, say, 70 heads, then you could assert that the
likelihood of the coin being fair is low–you could estimate that the
parameters of our coin model are not 50-50 and are instead 70-30.

Now for the task at hand. Think about the institutional goal of increasing
student retention; how might we approach our analysis of student data to
achieve this? Should we work out a means of calculating the probability
that a student is retained, or should we focus on determining the
likelihood that a student leaves? For your approach, what types of data
would you need and how would you use each element? Feel free to bring your
ideas to OIE’s next coffee hour or stay tuned for our analysis in next
month’s newsletter!


Thank you for reading this month’s edition of Data Matters. We look forward
to continuing the conversation with you at an upcoming OIE event.

The Office of Institutional Effectiveness

Erin Campbell | Associate Director, Coordinator for Institutional Research

Patrick Carter | Undergraduate Intern

Kylie King | Director

Levi Martinez | Data Analyst

Miranda Van Ness | Graduate Intern


--
*Institutional effectiveness*
Office of the President
Kehoe 313B
101 Broad Street
Plattsburgh, NY 12901
plattsburgh.edu
*Have an idea? Share it here!* <https://bit.ly/3yYzLkN>

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