Dimensions and Filters

Transcript
In this tutorial, we discuss two important functions of the National Student Clearinghouse’s Postsecondary Data Partnership dashboards – dimensions and filters.

Dimensions are an important feature of the PDP dashboards that allow you to disaggregate one or more of the dashboard visualizations by student subpopulations to help identify achievement or equity gaps.​

Applying a dimension to a PDP dashboard effectively 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.​

The main purpose of dimensions is to identify achievement or equity gaps.  Here, we show the percentage of students meeting the credit threshold in their first-year of college.​

When we apply the Age Group dimension, we see that the single line separates into four lines – one for each age category.  In 2018-19, we see that 26.8% of students older than 24 years met the credit threshold compared to approximately 21% of younger students. That is a gap of nearly 6 percentage points.​

By understanding which student populations lag, we can more effectively target our interventions.   In this case, for academic advisors of younger students, we could work with them to discuss the importance of completing sufficient credit each year for on-time completions.

Here is the list of dimensions common across all PDP dashboards.​

• Overall is the default dimension and does not segment the population.​
• Cohort Term segments 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 three age categories: 20 or younger, 20-24 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 not known for that student.​
• First Generation segments 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.  For example, if you would like to understand the outcomes of your students of color, you could filter by race/ethnicity and explore metrics like retention/persistence rates and completion rates. ​

You can also add multiple filters to more precisely define the student population like high academically performing, first-generation, students of color.​

Applying a filter to a PDP dashboard effectively removes students who don’t fit the criteria.  For example, if we want to focus on high-performing students of color we would filter out white students and students whose GPA is less than 3.0.

The main purpose of filters is to focus our attention on a student subpopulation.  Here, we show percentage of the student cohort who met the credit threshold in their first-year of college. In 2018-19, that percentage was 22.1%.​

Now, let’s add two filters to these data.  First, we will add the Race/Ethnicity filter and select Asian, Black or African American, and Hispanic students. Then, we will add the GPA Range filter and select GPA ranges 3.0 to 3.5, 3.5 to 4.0, and 4.0 to 4.5.  ​

This line chart shows the percentage of high achieving students of color who met the credit threshold in their first-year of college. In 2018-19, that percentage was 35.5% which is over 13 percentage points higher than all first-year students.

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 lets 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 of color, then ​we would filter out white students and students whose GPA was less than 3.0. ​

​Then, we would add the Age Group dimension.  This would segment our high-performing students of color 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 two 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 of color with a 3.0 GPA or higher.​

And this is the credit accumulation rate for high-academically achieving students of color disaggregated by Age Group.  In 2018-19, 36.9% of high-achieving students of color who are 20 years old or younger met the credit threshold compared to 32.5% of older high-achieving students of color. ​

In summary, both dimensions and filters are important functions of the PDP dashboards.  ​

Dimensions disaggregate dashboard visualizations to help us identify achievement or equity 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.