Get an overview of the PDP Financial Aid Dashboard, including unique filters available in this dashboard.

This session is intended for post-secondary institutions active with Postsecondary Data Partnership.

Amit is his institution’s Director of Student Financial Aid.

Recently, he was granted access to his institution’s Financial Aid dashboard, which is accessed separately the other Postsecondary Data Partnership (or PDP) dashboards. He advocated for the addition of financial aid data and is eager to learn more. Before jumping to the dashboard, Amit reviews some of the functionality and additions.

The overall purpose of the Financial Aid dashboard is to provide information about student unmet financial need.

In addition, filters and dimensions related to aid type, income, and dependency help identify equity gaps.

There are three unique additions to the Financial Aid dashboard. First is the Academic Year parameter.

While the dashboard is cohort-based, like other PDP dashboards, this dashboard allows the user to examine different academic years for the given cohorts.

Because of this, the user can choose which academic year to explore.

For example, the first academic year refers to the student’s first year at the institution regardless of the number of credit hours earned.

The second academic year refers to the student’s second year at the institution based on the time since the student’s first enrollment at the institution.

The dashboard includes up to six cohorts, and the user can review the first through the sixth academic year of those cohorts, when available. However, as the user progresses in academic years, the number of available cohorts decrease because data might not be old enough, or time has not progressed far enough, for their data to exist comprehensively in their sixth year. For example, for the 2021-2022 cohort, not enough time has passed for six academic years of data to be available.

Additionally, there are two unique filters on the Financial Aid dashboard. The first is Income.

Income is the total adjusted gross income, as reported to FAFSA, for independent students or parents of dependent students

However, Amit's institution did not have this data available from FAFSA, so per PDP data submission guidelines, he used an internal source to provide income information similar to FAFSA.

Data are grouped into categories for reporting purposes.

The second unique filter is Dependency status, which is the student’s status at the time of their FAFSA application or first term of enrollment for the academic year, if FAFSA data is not available.

Now, Amit is ready to explore the dashboard. He logs onto the Postsecondary Data Partnership dashboards and clicks on the Financial Aid dashboard.

At the top of the dashboard, he finds the “Select Academic Year” filter. It defaults to the first academic year, meaning the students who are in their first year at his institution.

He clicks on the filter’s drop down menu to see the list of other academic years.

To the right of that filter is, “Apply Additional Filters.” Opening that, Amit finds filters that are common to the PDP dashboards like Gender and Race/ethnicity. In addition, he finds the new filters for Income and Dependency.

Now, let’s explore the visuals. Below the filters, Amit finds the first reporting section. This section focuses on trends averaged across the available cohorts in the first academic year, which are displayed in three “call-out” boxes. The first is, “Average percentage of students who applied for federal financial aid (ISIR Record) in the first academic year.”

For our institution, every student in our first academic year, across all of the cohorts, has applied for federal financial aid.

Next, “Average percent of students with an unmet need above $500 in the first academic year.” For our institution, 76% of our students across all the cohorts in their first academic year have an unmet need above $500.

And the last value is “Average unmet need amount in the first academic year.” For our institution, the average unmet need for students across all of the cohorts in their first academic year is $5,972.

Amit notices that, if he changes the Academic Year filter to the 2nd academic year, the values change in this section to reflect the students, in the available cohorts, for their second academic year. He changes that filter back to first academic year and explores the next reporting section.

Amit scrolls down the screen and finds the next set of reports, which are stacked bar charts.

The title of the upper chart is “Average Cost of Attendance in the 1st Academic Year.”

The x-axis shows three years of data for students in their first academic year but…

… if Amit changes the academic year to the second, the most recent year is removed.

And, if Amit changes the academic year filter to the third year, the report drops the second bar. The reason the report removes these bars is that sufficient time has not elapsed for that data to be shown.

Amit resets the Academic Year filter back to the first year. Now, he looks at the data.

At the top, he sees a horizontal line; this represents the average total cost of attendance for that cohort in their first academic year. The top segment in the stacked bar chart is EFC which stands for Expected Family Contribution. The middle segment is Grant Aid, and the bottom section is the Unmet Need.

This chart allows him to compare these four metrics, across up to six cohort years, for students in their first academic year to see whether the cost of attendance has changed at a different rate compared to the students’ EFC or Grant Aid.

Now, he looks at the lower stacked bar chart, which is the “Cost of Attendance by Dimension.” He notices that this chart reports the same data as the upper chart except that it is disaggregated by the first dimension, which is “Enrollment Type.” The lower bar chart has a stacked bar for each of the dimension’s categories, which are First-time and Transfer-in, for each cohort represented.

The data represented in the chart are for the same four metrics: Cost of Attendance, EFC, Grant Aid, and Unmet Need.

Next, Amit clicks on the Race/ethnicity dimension and sees that the “Cost of Attendance by Dimension” bar chart disaggregates for each race/ethnicity category in each cohort.

To simplify the chart, Amit applies the Race/Ethnicity filter at the top of the dashboard to only include Black or African American, Hispanic, and White students, as he is currently focused on studying the three largest groups. He sees that the Cost of Attendance by Race/Ethnicity and the Average Cost of Attendance in the 1st Academic Year visual now includes only those three populations.

Other dimensions Amit can apply include attendance (which is full-time or part-time status), age group, gender, first-generation, GPA, Income, and Dependency.

Now, Amit looks at the data. Scanning across the race/ethnicity categories, he sees different bar heights, which tells him that the average cost of attendance varies by race/ethnicity group. He also scans across and sees different bar heights for unmet need, which tells him that some race/ethnicity groups have a much larger unmet need than other groups.

Besides adding a filter to reduce the amount of data showing, Amit can also click on one of the segments in the upper bar chart, or the corresponding item in the legend, which effects the bottom bar chart since these bar charts are linked.

For example, he clicks on an Unmet Need in the legend…

…which changes the linked bar chart to display only Unmet Need. This makes it easier to identify student groups with a higher unmet need.

As another example, Amit clicks on the Grant Aid segment in the legend, and it filters the lower bar chart to Grant Aid.

To remove that selection on the Cost of Attendance bar charts, Amit clicks on the “Cost of Attendance” icon in the legend.

To reset the dashboard back to the first academic year and remove any global filters, Amit scrolls to the top of the dashboard and clicks “Reset View.”

Amit scrolls back down the dashboard to find the second set of bar charts that are also linked to each other.

The upper chart is titled, “Share of Students with Unmet Need in the First Academic Year.”

The upper band represents students who are “over-aided,” meaning their aid package is more than their cost of attendance. For his institution, approximately 23% of students in their first academic year are over-aided for each of the three cohorts shown. The $1 to $500 band shows the percentage of students in their first academic year with an unmet need up to $500. For his institution, approximately 1% of students are in this band regardless of cohort. In the next band, approximately 9% of students in their first year have an unmet need between $501 and $2,500. In this band, approximately 11% of students in their first year have an unmet need between $2,501 and $5,000. This band represents first-year students with an unmet need between $5,001 and $15,000 regardless of cohort. And the last band represents first-year students with more than $15,000 of unmet need.

Now, he looks at the lower chart. By default, this data is disaggregated by the first dimension, which is Enrollment Type. The data in the lower bar chart defaults to the first category from the upper chart, which is over-aided students.

However, by clicking on another segment in the upper chart, or by clicking the corresponding icon in the legend, Amit can change that report to show any of the unmet need categories. Amit clicks on the bottom segment…

…and the lower bar changes to display those students with more than $15,000 of unmet need. This makes it easier to identify the student groups with the highest need.

Next, he changes the dimension to First-Generation to show how the lower bar chart changes to report those categories.

Now that Amit knows how to navigate the dashboard and read the data, he’s excited to get started exploring his institution’s data. To make the data even more robust, Amit decides to add the additional three cohorts’ information so that all six possible cohorts of data display. He will also explore other questions, including the unmet need of other race/ethnicities and small populations (those with small Ns).

In summary, the Financial Aid PDP dashboard provides critical information to help institutions…

…better understand the cost of attendance, expected family contribution, grant aid, and the unmet need of their students based on academic year enrolled.

Identify the characteristics of students with unmet need

And identify if there are equity gaps in cost of attendance and financial aid, including grant aid and unmet need.

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