Exploring Your Sense of Belonging Data

Developed by Ciji Heiser, Ph.D., with contributions from Mentor Collective

For almost 50 years, the importance of student belonging has circulated throughout higher education as a key factor in student success (Tinto, 1975; 2012). Research supports the finding that students with peer mentors report higher levels of belonging (Ayokanmbi, 2021; Liu, Chen, Hamilton, & Harris, 2022; Lapon & Buddington, 2024; Strayhorn, 2008). Additionally, research suggests that students in mentoring relationships have higher self-efficacy, GPA, resource utilization, persistence and retention (Clark & Crome, 2004; Colvin & Ashman, 2010; DeMarinis et al., 2017; Mahan et al., 2014; Snowden & Hardy, 2013). 

Multiple studies have found that sense of belonging is also connected to identity (Bettez, 2010; Brandy, Cohen, Jarvis, & Walton, 2020; Rainey et al., 2018; Strayhorn, 2018) such that mentoring relationships are associated with increased academic progress and reduced symptoms of distress for underrepresented and first-generation college students (Hurd, Tan, & Loeb, 2016). Brady, Cohen, Jarvis, & Walton (2020) found that mentoring relationships increase belonging outcomes for black students with positive effects lasting beyond college and into adulthood.

This guide suggests multiple approaches for institutions to foster meaning-making and enhance impact with their data on sense of belonging. 

Where Does This Data Come From?

Several of the Mentor Collective assessment surveys ask mentees and mentors three questions related to sense of belonging. The surveys that ask sense of belonging questions include Strategic Enrollment, Student Success, and Adult Online. Student responses to each individual question, and aggregates across questions, are showcased on each institution’s Partner Dashboard. Sense of belonging data provides timely, actionable information as a leading indicator of student success. 

Mentee and Mentor Statements included on the start of program survey assessment are: 

• I feel comfortable at my school. 
• I feel like I am an important member of my school. 
• I am supported at my school. 

Students use a 5-point likert scale, where 1=low and 5=high, to respond.

You Have a Dashboard and Data Exports, Now What?

Suggestion 1: Set a meaningful target.

Use the recent data from across Mentor Collective participating institutions to establish a meaningful target for sense of belonging in your peer mentoring program. Select a target to serve as a benchmark for both mentor and mentee sense of belonging. 

• What is your target for student responses to the three sense of belonging statements?
• What is your target response to the average of all three sense of belonging statements? 
• What range of responses signals a moment to celebrate (e.g., average response of 4.5-5)?
• What range of responses signals the need for intervention (e.g., average response of 1-3)?

Suggestion 2: Reflect on responses.

Reflect on student sense of belonging responses for mentees, mentors, and demographic groups meaningful to your campus (e.g., by gender, race, and first-generation college student status). 

• What do you notice overall about  student responses to each sense of belonging statement?
• What do you notice about the average of all three belonging statements?
• For mentees, which sense of belonging statement responses are  higher than your target? Lower than your target? 
• For mentors, which sense of belonging statement responses are  higher than your target? Lower than your target? 
• For each demographic group, which sense of belonging statement responses are higher than your target? Lower than your target? 
• Where do you have opportunities to celebrate sense of belonging?
• Where do you have opportunities to provide interventions to enhance sense of belonging?

Suggestion 3: Explore relationships between variables.

Explore the relationship between sense of belonging and academic achievement for students in your mentoring program. Given prior research, we recommend exploring the relationships between sense of belonging and  self-efficacy, GPA, resource utilization, persistence, and retention in your mentoring program data (Clark & Crome, 2004; Colvin & Ashman, 2010; DeMarinis et al., 2017; Mahan et al., 2014). Additional research suggest, sense of belonging coupled with resource utilization can result in help seeking behavior, an additional dimension covered on the Mentor Collective survey (Bharadwaj, Shaw, Condon, Rich, Janson, & Bryant, 2023). Of note is that the recommendation is not to explore all of these relationships; however, analysis strategies (discussed further below) for examining these relationships are very similar. 

Sense of Belonging and Self-Efficacy

• For students who are mentored and matched: what is the most recent average self-efficacy score for students with an average sense of belonging score below 3, at 3, above 3. 
• What differences or relationships do you notice? 
  • Do students with higher average sense of belonging also have high academic self-efficacy?

Sense of Belonging and Help Seeking

• For students who are mentored and matched: what is the most recent average academic help seeking score for students with an average sense of belonging score below 3, at 3, above 3. 

Sense of Belonging and Term G.P.A.

• For students who are mentored and matched: what is the average term G.P.A. for students with an average sense of belonging score below 3, at 3, above 3. 
• What differences or relationships do you notice? 
  • Do students with higher average sense of belonging also have higher term G.P.As? 

Sense of Belonging and Persistence: Term to Term Enrollment

• For students who are mentored and matched: what is the persistence rate  for students with an average sense of belonging score below 3, at 3, above 3 . 

Sense of Belonging and Retnetion: Fall to Fall Enrollment

• For students who are mentored and matched: what is the retention rate  for students with an average sense of belonging score below 3, at 3, above 3.  

Suggestion 4: Create an action plan.

Create an action plan for enhanced impact that includes necessary interventions, celebrating target achievement, and shares the program impact with key campus partners. Action plans are simple but effective tools which focus time and talent on using key data findings to inform 1-2 action strategies to enhance impact while simultaneously building in follow up to determine if the action strategies were effective. Below is an example of what an action plan could look like.

Impact Analysis Steps

Step 1: Clearly identify the populations and the timeframe.

Start with a detailed description of which students were invited to participate in the mentoring program. For example, we invited transfer students with less than 45 credit hours to engage in the peer mentoring program. Defining the population prior to analysis, allows you to quickly determine how you will make meaning of the data once analysis is completed. 

Next, define meaningful timeframes for analysis. For example, when talking about persistence, specify that the analysis includes students enrolled in fall of 2023 and spring of 2024. Retention could be defined as enrolled in fall of 2023 and enrolled in fall of 2024. 

Step 2: Identify the relevant fields needed to conduct your analysis.

The fields needed to conduct the analysis strategies recommended above are provided in the table below. In many cases, you will need to pull fields from multiple sources, and having a clear list of fields  with definitions (also known as a data dictionary) will help to gather the relevant information more easily. 

A template for populating these fields is provided here. Columns on tab “Step 2 Fields” that are denoted in blue (A, B, C, G, H, I, J) are from the Mentor Collective participant data export while the columns in yellow (D, E, F, K, L, M, N) would come from the student information system (SIS). Here’s how to export your participant data through your Partner Dashboard.

Step 3: Obtain and merge data.

Because you pulled fields from multiple sources, you will now need to merge the data into one data source for analysis. This is possible by identifying a unique, common data point for each individual that exists in all data sources, and conducting a VLOOKUP or XLOOKUP to pull related data. The recommended common data points are: institutional email address and institution ID*. An example of merged data can be seen here.

Step 4: Create a pivot table.

Use your fields of interest in the template provided here and pictured below.  As a guiding example, we will explore sense of belonging and academic self-efficacy. Below, we will walk you through how to compare students’ most recent average sense of belonging scores with students’ most recent average academic self-efficacy scores to identify if there are clear trends or relationships between these two topics associated with student success.

Step 5: Reduce the data in the pivot table to identify meaningful findings.

Below is a screenshot of a reduced pivot table. You can find the template here.

In the sense of belonging template, you have access to a summary tab. An example of a completed summary table comparing students’ most recent average sense of belonging scores and most recent average academic self-efficacy scores would look like the table below. 

Below is a screenshot of a reduced pivot table with the fields average sense of belonging and term G.P.A.. You can find the template here. In this example, we created bands for the G.P.A. in order to reduce the volume of data. We suggest using bands commonly used or understood on your campus to reduce your data if you take this approach. 

For persistence and retention comparisons across a sense of belonging, it is helpful to generate a rate.  There is a summary table for persistence included in the spreadsheet guidelines and provided below as an example of this data could be calculated and shared. 

Step 6: Articulate impact statements.

Bulleted below are examples of impact statements that could be made with the data in the table above. 

• Mentored students who report an average sense of belonging score above a 3 are substantially more likely to also report a self-efficacy score above 3. 
• Our data signals a clear relationship between increased sense of belonging and increased belief in their ability to be academically successful. 
• When students feel they belong, they also feel they can be academically successful.
• Mentoring helps students feel like they belong and can improve a student’s belief in their ability to be successful, also known as academic self-efficacy.

Now That You Have Your Exploratory Findings, What Can You What Can You Do?

Idea 1: Share the data!

Take your data on a road show by asking to present at committee meetings or standing meetings across campus (e.g., Provost’s council, President’s Cabinet, Student Government, Faculty Senate). For example, mentorship program directors at Florida Atlantic University collaborated with their institutional research office to identify how mentorship had made an impact for specific groups of students. They shared these findings with top leadership at the University, leading to increased and ongoing monetary support for their mentorship initiatives across the institution. Sharing data is one way to promote an institution-wide culture of mentorship.

Idea 2: Shift from sharing to collective problem solving.

Create a space for collective problem-solving or idea storming with those most closely connected to the data, including students. Host a data and doughnuts party and ask attendees to reflect on: "What do you see in this data?" and "How might we respond to these insights?" Augusta University has a committee with staff and faculty from across the institution to do this. Among their initiatives include working to ensure that mentorship is leveraged in the right ways in response to student needs.

Idea 3: Identify areas to celebrate.

Take the time to identify and highlight areas where your organization is making strides towards its goals. What does the data reveal about progress? What surprising insights are worthy of celebration?

Idea 4: Empower others with knowledge.

Who else might find this information meaningful? Consider reaching out to community partners, student clubs, or historically underrepresented groups to talk about the data. Sharing data with a wide range of collaborators can lead to fresh perspectives and more comprehensive decision-making. This is one way to ensure a participant-centered approach to mentorship. 

For more discussion on how to share your sense of belonging data in meaningful ways across your institution, watch our webinar on the topic.

Ciji Heiser, Ph.D.

Ciji Heiser, Ph.D., (she/her) is the Founder of Co-Creating Action, an award winning researcher and seasoned professional in education, evaluation, and strategic planning. She teaches antiracist methodologies at American University, contemporary issues in higher education at New England College, and Applying and Leading Assessment in Higher Education for the Student Affairs Assessment Leaders. She holds degrees from Bucknell University, Kent State University, and the University of North Carolina at Greensboro.

She has led assessment, evaluation, and strategic planning initiatives across sectors. Recently, she contributed to NSF grants supporting underrepresented students in STEM and conducted qualitative research on factors influencing students’ choices in pursuing postsecondary education. Currently, she collaborates on reducing racial disparities in prison diversion programs across Illinois and is researching how policies shape equal opportunity work across a state system of community colleges. 

As a volunteer, Ciji co-leads the Grand Challenges in Higher Education plan, leveraging data to promote accessible and quality education. She also shared her expertise as a faculty member at the ACPA Assessment Institute and as past-chair of the Student Affairs Assessment Leaders.

Ciji developed the impact analysis guidelines, as well as the recommendations for using the analysis data to create institutional impact.