Use the Financial Aid Analysis-Ready File to Identify Students with Unmet Financial Need

Follow a four-year institution as they use the financial aid analysis-ready file to identify students with unmet needs.

Transcript Joshua, our institution’s Director of Enrollment Management, recently received a national report released by his professional association, showing the need for institutions to develop and implement an emergency financial assistance program to help students cover small, unexpected expenses like car repairs or healthcare costs. Before he considers creating a program, he wants to better understand the financial needs among his students. Joshua meets with Prachi, the Director of Student Financial Aid to talk about the information Joshua needs to make an informed decision regarding whether to implement an assistance program. Here’s the list of questions they wrote.

• What percentage of students have unmet financial need?
• Of students with unmet financial need, what is the average unmet financial need?
• Where do they live?
• What is their dependency status?
• If students are dependent, what is their expected family contribution?
• What is the relationship between unmet financial need and retention?

Prachi goes back to her office and asks Navita, the Assistant Director of Student Financial Aid, and their unit’s analyst, to research Joshua’s questions. To get started, Navita accesses the Clearinghouse’s secure FTP site and downloads the Financial Aid Analysis-Ready File. The first seven metrics provide information about the student and institution like student name, date of birth, and student ID. The next three metrics provide information about the cohort like cohort term and student age. And the final set of metrics give information on financial aid like dependency status, housing status, cost of attendance, grants, and unmet need. Comparing those metrics with Joshua’s data request, she realizes that some of those questions require data that are not in the Financial Aid Analysis-Ready File but are included in the Cohort Analysis-Ready File. She downloads that data file from the Clearinghouse’s secure FTP site, and merges the datasets together using Student ID which is a key field in both datasets. Now she is ready to begin. Joshua’s first question is, “What percentage of students have unmet financial need?” Navita uses “Cohort” to filter to the most recent cohort. Then, she analyzes the field “Unmet Need” to find that 21% of their students have unmet financial need. Joshua’s second question is, “Of students with unmet financial need, what is the average amount?" Navita uses the field, Unmet Need, to remove students with no unmet need and averages the remaining data to find that, on average, these students have $1,263 unmet financial need. Joshua’s next question is, “Of students with unmet financial need, where do they live? Keeping those same filters in place, Navita analyzes the field “Housing Status”. She learns that,

• 68% live off-campus but not with family,
• 23% live off-campus with family, and
• 9% live on-campus.

The next question is, “If students are dependent, what is their expected family contribution?” Navita filters out the students who are independent using the “Dependency Status” filter. Then using the EFC field, she finds that the average family contribution is $598. Navita removes the “Dependency Status” filter to answer the final, and most important, question, “What is the relationship between unmet financial need and retention? She analyzes the field called Unmet Need with the field from the Cohort-Level Analysis-Ready file called “Retention” to learn that 47% of students, with more than $500 of unmet need, did not return for their second year of college. A week later, Navita meets with Joshua to share the results. Joshua decides to develop and implement an emergency financial assistance program designed to help any student with short-term financial need. In summary, the Financial Aid Analysis-Ready file can be used to better understand the types of financial aid, and need, among students. These data can be merged with other Analysis-Ready files or institutional data to construct a powerful student success research dataset. Analyses of these data can help identify patterns among students with unmet need to construct better support systems.