Dimensions and Filters (6 minutes)

Learn about the two important functions of the National Student Clearinghouse’s Postsecondary Data Partnership dashboards — dimensions and filters.

Transcript In this tutorial, we discussed 2 important functions of the National Student Clearinghouse's Post Secondary Data Partnership Dashboards, dimensions and filters. Dimensions allow you to disaggregate one or more of the dashboard visualizations by student subpopulations to help identify achievement gaps. Applying a dimension to a PDP dashboard segments the student cohort into unique categories depending on the characteristics of that student. For example, if we apply the age group dimension, we assign each student to one of three categories:

• those who are 20 years old or younger,
• those who are between 20 and 24 years old,
• and those who are older than 24.

Because we are segmenting our student population, we can't assign more than one dimension at a time. Here is the list of dimensions common across all PDP dashboards.

• Overall is the default dimension and does not segment the population.
• Cohort terms segment students by the academic term that the student enrolled at your institution. Options are fall, spring, summer, and winter.
• Credential Type sought is the credential that the student is seeking. The options include certificate seeking, associate seeking and bachelor's seeking.
• Attendance, which is a measure of intensity, segments by full time or part time student status.
• Dual summer enrollment segments, students who attends the institution as a dual enrolled student or a summer enrolled student before they began their credential seeking experience.
• Age group is divided into 3 categories 20 or younger, 20 to 24 years old or over 24.
• Race and ethnicity segments students by their racial or ethnic identity.
• Gender segments students into male, female, or unknown.
• Pell Grant recipient segments students as yes, the student has a Pell Grant. No, the student does not have a Pell Grant or unknown, meaning the Pell Grant status is unknown for that student.
• 1st generation segments the students as first generation, meaning they do not have a parent or guardian who graduated from college, not first generation meaning they have a parent or guardian with a college degree or unknown, meaning that their first generation status is not known.
• GPA range segments students in half point increments from zero GPA to 4.0 and above.
• The last two dimensions are math preparation and English preparation. Both segment students based on their readiness or preparation to take college level math or English courses. Your institution determines what readiness means.

In addition, some PDP dashboards may have unique dimensions. Now let's discuss another important feature of the PDP dashboards, which is the filter. Filters allow you to increase your understanding of the student experience for a specific student population. You can also add multiple filters to more precisely define the student population, like high academically performing first generation students. Applying a filter to a PDP dashboard removes students who don't fit the criteria. For example, if we want to focus on high performing students, we would filter out students whose GPA is less than a 3.0. The main purpose of filters is to focus our attention on a student's subpopulation. A wonderful feature of the PDP dashboards is the ability to apply a dimension and one or more filters to better understand the student experience. Remember that a dimension segments our students into categories, and adding filters let's us focus on a specific student population. The result is a filtered and segmented student population. For example, if this represents our first-year student population and we want to study the impact of age on high academically performing students, we would filter out students whose GPA was less than 3.0. Then we would add the age group dimensions. This would segment our high performing students into three groups:

• those who are younger than 20 years old,
• those between 20 and 24 years old,
• and those older than 24.

Let's see the effect of adding filters and a dimension to a data visualization. This is the credit accumulation rate for all first-year students. This is the credit accumulation rate filtered to students with a 3.0 GPA or higher. And this is the credit accumulation rate for high academically achieving students disaggregated by age group. In 2018-19, 36.9% of high achieving students who are 20 years old or younger met the credit threshold compared to 32.5% of older high achieving students. In summary, dimensions and filters are important functions of the PDP dashboards. Dimensions disaggregate visualizations to help us identify achievement gaps among student populations, and filters help us focus on a specific student population which supports A comprehensive assessment of those students’ experiences. This ends our tutorial. Thank you for joining us.