Earlier this year, NCAN presented “7 Ways to Transform Your NSC Data… And Your Work,” a webinar intended to impart tips and tricks for making better use of the National Student Clearinghouse (NSC)’s StudentTracker suite of services.
Student-level postsecondary outcomes data obtained through the National Student Clearinghouse’s StudentTracker platforms are an invaluable resource for understanding and improving program outcomes. NCAN members know that we have long suggested using the
NSC data to its furthest potential. Unfortunately, many organizations struggle with reaching that potential because working with the NSC data has a fairly steep learning curve.
Joining me on the webinar were Kimberly Hanauer, founder and CEO of UnlockED, and Todd Nolt, director of evaluation and analytics at the Vela Institute. Both of these
organizations are consultancies dedicated to helping organizations in the college access and success space employ data-driven strategies to improve students’ postsecondary outcomes. Both Todd and Kimberly are NSC data experts, and NCAN has had the
opportunity to work with them and their organizations. On a personal note, I was delighted and grateful they were willing to present with me.
Without further ado, a quick rundown of the topics covered on the webinar:
1. Squeeze more value from the aggregate report.
StudentTracker for High School (STHS) subscribers receive an aggregate report three times a year along with their detailed, student-level data. This aggregate report contains dozens of pre-constructed charts, graphs, and tables. One of those tables is
a list of the top 25 institutions where students in the report matriculate. It’s very useful!
Beyond giving quick, clear answers to questions like “which are our top two- and four-year institutions?” you can also connect the table to the National Center for Education Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) to see
those institutions’ completion rates. This is helpful because, for example, if the institution to which 40% of your students matriculate has a 25% six-year completion rate, that may change postsecondary advising practice and/or prompt a conversation
with the postsecondary institution itself on how to address those outcomes.
2. Make better use of the requestor return field.
The requestor return field is a column in a Student Tracker for Educational Organizations (STEO) /StudentTracker for Outreach (STOR) data file or STHS request file that operates as “free text.” Whatever characters a program wants can go in here, up to
a certain limit.
Programs can and should get creative by including program data here that they might want to sort/disaggregate by later (e.g., GPA, AP exam history, GEAR UP participation). Be sure to include a program unique ID to link to program attribute data (STOR/STEO
only, STHS users have a dedicated column for this in the graduates file). Don’t forget to separate values (e.g., with “;” or “,” or “&”). You need to be able to separate all the values you’re mashing in here on the back end. In Excel, use the
“Text to Columns” feature to do that.
The webinar goes even further to discuss how to use crosswalks to fit even more characters into the requestor return field.
3. Disaggregate outcomes by student characteristics.
“Disaggregate your outcomes” is hardly groundbreaking data analysis advice. NCAN members have been doing it for years by gender, race, ethnicity, and first-generation status, among other demographic characteristics.
But Kimberly Hanauer makes a strong point that disaggregating by, for example, SAT scores or GPA is beneficial. First, it just shows where your students are distributed across those characteristics; do you have a lot of students with academic profiles
in the middle, or do you have a bimodal distribution with lots of students on either end of the distribution, or do you have another pattern? After understanding that distribution (which you can do before linking to NSC data), overlay those data with
postsecondary enrollment, completion, etc., from NSC. This will shed light on where gaps or disparities exist that programs can then address through practice.
The student-level data from the NSC detail report can be difficult to work with, but applying basic techniques can yield valuable shortcuts.
Most Excel users know about the “Filter” functionality. The Vela Institute’s Todd Nolt uses this with the detail report to filter on various variables. For example, filtering on the “Enrollment Begin” column can determine currently enrolled students with
minimal fuss. Filtering “College Name” helps to create quick lists of students attending specific institutions. Of course, stacking multiple filters allows for even deeper dives.
6. Don’t miss your dual-enrolled students.
NCAN members working with dual-enrolled students often have trouble capturing these students’ outcomes. For example, students who have postsecondary enrollments before high school graduation may not have all of those enrollments reported in the student-level
detail file.
The webinar offered two approaches for ensuring you get these students’ data:
STEO and STOR users: Set the search begin date (STEO/STOR) to an earlier year. Setting to the end of the student’s freshman year ensures you will capture dual enrollment. It is important to specify the actual graduating class
in the requestor return field so you can put the student in the right class during data analysis.
STHS users: submit a separate request file for dual-enrolled students. Keep actual graduation years in your graduates file. Run a separate request file of dual-enrolled students where graduation date is set earlier. Find more on request files here.
7. Calculate summer melt.
More and more NCAN member programs are turning their attention to stopping summer melt, the phenomenon whereby students who are college-intending at high school graduation don’t matriculate the following fall. Measuring summer melt is pretty tricky, though.
This is a more involved process than the others on this list.
Calculating summer melt does require one particular variable that’s outside of the NSC data itself: students’ postsecondary intentions (probably obtained through a senior survey).
On the senior survey, take the top 25 institutions list mentioned above, and include those institutions (as named by the NSC) plus an “other (specify)” option. Then, when the NSC data come back, combine the postsecondary intention data and the student’s
first institution (if any) into one spreadsheet, and compare where a student intended to matriculate with where they actually did.
To calculate summer melt, you need the percentage of all seniors who intended to matriculate and then the percentage of all students who either attended anywhere at all or attended where they intended (depending on your organization’s specific definition
of summer melt). The difference between those two percentages is a rough approximation of the summer melt rate.
8. Bonus! Engage stakeholders with interactive visualizations.
Data analysis and reporting are all well and good. No, seriously, there’s no “but” coming, those things are all well and good. What makes them better, though, is clean, visually appealing data visualization.
In this section of the webinar, Todd Nolt shared some screenshots from Vela’s work as a reminder that connecting NSC data to a data visualization platform like Microsoft PowerBI, Tableau, or Google Studio can make them even more insightful and valuable
for users.
Thank you again to Kimberly and Todd for lending us their expertise. Want to learn more about NSC data or have a project they might be able to help with? Be sure to reach out to them.
NCAN has a strong commitment to this kind of professional development and technical assistance around the National Student Clearinghouse data. NCAN members who would like to learn more should contact Bill DeBaun, Director of Data and Evaluation, at debaunb@ncan.org. Stay tuned for additional webinars with even more tips and tricks.
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