About Our Data 

What We Collect

Data Collection

National Student Clearinghouse data on student enrollment, academic progress, and educational outcomes is extensive. Nearly 3,600 colleges and universities, accounting for 97% of higher education enrollments in the United States, regularly submit their data to us. We also receive data from over 22,000 high schools, enabling us to show if and where their graduates go to college and how they do once they get there.

Equity insights

The Clearinghouse gives institutions the ability to filter and analyze results by race and ethnicity, gender, age, Pell Grant status, and more, enabling insights into intersectional experiences in education. In addition, the Postsecondary Data Partnership (PDP) enables educators to analyze these data for their own students and identify which subpopulations of students need support and when with a strong focus on intersectionality as well as examining and addressing equity gaps in student success. The PDP also supports analysis of additional student characteristics — like first-generation status — which can provide insights on institutional trends and students’ entire academic journeys. The Clearinghouse’s high school submissions provide additional information that we can use to identify impacts and outcomes by income level and “urbanicity” (i.e., how urban or rural a student’s high school locale is).

“The Clearinghouse recognizes that how we work with data must reflect our values and believes that equity should be at the center of any data collection, acquisition, or usage.” 

— The Clearinghouse Equity in Research and Analytics 

How We Collect Our Data

Every college, university, and high school voluntarily decides whether to submit its student data to the Clearinghouse. Schools use our systems to submit their data securely and electronically via secure file transfer protocol. Once the Clearinghouse receives a school’s file, we validate the data to identify potential errors and work with the school to resolve them before loading the data into our database.

Since 2008, the Clearinghouse has also given participating institutions the option of including 13 additional data elements in their enrollment submissions. Several of these data elements are essential to providing an equity perspective into education outcomes. The Clearinghouse’s ability to bring an equity focus to education outcomes was enhanced with the PDP data submission, which launched in 2017.

How We Use This Data

The National Student Clearinghouse^® Research Center™ uses data to develop national reports made available free of charge through its website. The Clearinghouse also offers tools and resources that institutions can use for their own analysis and decision-making on DEI issues. These products include: 

• The Postsecondary Data Partnership 
• Student Insights Dashboard  
• StudentTracker^® for Colleges and Universities
• StudentTracker^® for Educational Organizations 
• StudentTracker^® for High Schools 

Using these tools, institutions can gain a better understanding of outcomes for different student groups, which is a prerequisite to equitable education.

DEI Data Limitations and How We’re Addressing Them

There are some constraints on the DEI data we can collect and share, in part related to the Clearinghouse’s data collection history. At our founding by the higher education community the Clearinghouse collected student enrollment data solely for administrative purposes — to serve the student record management and reporting needs of the nation’s colleges and universities. The Clearinghouse has evolved to serve a broader purpose, and its equity-related data has expanded greatly as well. But there are still some limitations on these data: 

• Race and ethnicity. Data for these categories are reported voluntarily, so the reporting rate is not as high as we would like. For the 2020–2021 academic year, race and ethnicity were reported for 62.1% of the enrollments submitted to the Clearinghouse. The Research Center reports have a much higher coverage rate for the race and ethnicity data because we can combine the data from multiple services for the aggregate results. Nonetheless, no one service of the Clearinghouse has a complete or near complete coverage. 
• Gender. As another voluntary reporting category, gender has a reporting rate of 63.6% of enrollments. Through the Clearinghouse Research Center’s process of “gender imputation” based on student names, we have been able to populate this data element for 91% of enrollment records. However, we recognize that these designations are binary only, and we are working to make our gender designation process more accurate and inclusive.  
• Socioeconomic status. For students at participating PDP institutions, we can use whether a learner is a Pell Grant recipient as a proxy for socioeconomic status. We have also created a high-school poverty level measure based on the percentage of students who are on free or reduced-price lunch at that high school. The Research Center is working to develop a new proxy for socioeconomic status that can be used across many publications. 
• Additional data points. The Clearinghouse strives to collect data on military status, parental level of education, disability status, and more. However, we understand that these data can be sensitive and are often difficult for institutions to collect or share. We are actively working with institutions to better discern the optimal method for gaining insights from institutional reporting on these data.

To address these and other limitations on DEI data collection and use, the Clearinghouse established an Equity in Research and Analytics Unit in late 2021. This unit will investigate, establish, and advance principles of equity, inclusivity, and freedom from bias to inform the Clearinghouse’s approach to data collection and analytics.

Clearinghouse Data Equity Principles

Over the past few years, the Clearinghouse has focused on ways to be more intentional about DEI, both in our own workplace and in our function as an organizational data hub.

As this effort developed, we realized that we need to be aware of the biases that may exist in data, which can contribute to inequities despite our best efforts. We also recognized that providing data-driven insights, while important for raising awareness, will not, on its own, lead to closing equity gaps.

When we learned of the data equity principles developed by the Education-to-Workforce (E-W) Indicator Framework initiative, we saw an opportunity to bring together our action plans under the framework’s principles to achieve greater impact. These data equity principles, which will guide our work moving forward, include the following: 

• Employ ethical behavior to (1) respect the rights of individuals who provide data, (2) promote equity and well-being, and (3) minimize the risk of harm.
• Protect the privacy of individuals who provide data, while ensuring appropriate ownership of and access to information.
• Disaggregate data on outcomes and system conditions to analyze disparities, monitor progress, and guide action.
• Examine social and historical contexts to identify root causes of disparities, inform data collection and use, and develop data-driven solutions.
• Question conventional, default methods of — and assumptions in — data collection and analysis and supplement quantitative data with information from other sources.
• Ensure that data visualizations promote inclusion and awareness across culturally, linguistically, and racially diverse audiences.
• Restore communities as data experts, using culturally responsive approaches to engagement and co-creation that support equitable data use.

Read the full text of the E-W Indicator Framework for more information.

Establishing Equity Goals and Developing Expertise

In recent months, the Clearinghouse has focused on developing expertise on data equity among staff members and has identified near-term and long-term goals for this line of work. 

Near-term goal 

Our near-term goal is to review all Clearinghouse metrics, reports, and publications through an equity lens. Here are some of the actions we will take as part of that review:

• Analyze data for potential biases
• Remove and replace biased data labeling
• Bring asset-based framing to our analysis and reporting of outcomes
• Identify additional data that may speak to the context behind disparities, bringing an equitable approach to all data visualizations

Long-term goals

Our long-term goals include:

• Operationalizing our vision for data equity
• Monitoring our progress in implementing data equity principles
• Producing guidelines on equitable data use for our external partners and helping the institutions we work with go beyond simply “knowing” that disparities exist
• Analyzing and understanding the conditions that may produce disparities