Halls as Home: Measuring Belonging for Your Residents

Belonging is a continued focus of Student Affairs research, often looking at how students feel belonging to institutions. Housing is often looked at on college campuses as a supercharger for belonging, when I did a quick online search I noticed that belonging or connection came up in every single Resident Advisor posting I looked at. UGA even has a student staff role called Resident Belonging Assistants. 

If you are not measuring belonging on your campus, welcome to a step-by-step guide to measuring, calculating, and reporting on student belonging in your residence halls. Please double check that belonging isn’t already being measured on your campus before collecting this data, we want to avoid putting a burden on students for their data if we already have it!

The Belonging Barometer

My favorite belonging tool comes from The Belonging Barometer, published by Over Zero and the American Immigration Council back in 2024. I am a data nerd and think this is one of the best reports I have read about belonging, it is both informative and accessible. I highly recommend giving it a read if you are looking to understand how you could measure belonging on your own campus. A few reasons that I love The Belonging Barometer include: it is written in a way that it could be used anywhere in the United States, it includes the entire tool at multiple levels and how to calculate belonging, and it includes national benchmarks for comparison. Now when I say levels, I mean that The Belonging Barometer looked at belonging in families, friendships, workplaces, local communities, and the nation. We are going to just look at the local communities level. One of the findings from The Belonging Barometer study was that experiencing greater levels of local belonging was associated with more civic and social cohesion. 

Measuring Belonging in Local Communities

The Belonging Barometer uses this question for measuring belonging in local communities. Now, I would recommend defining the local community here. Do you want to look at the city or neighborhood you are in? Do you want to look at your whole campus? Do you want to look at housing as a whole? Do you want to look at your specific section of campus? Do you want to look at your specific building? That is up to you. This tool was built for looking at communities like a city overall, but I feel confident using it looking more microscopic. I do not recommend looking at a level smaller than a building. For this guide, I am going to look at Monteith Hall.

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Think about your relationship with the staff and other residents in Monteith Hall To what extent do you agree with the following statements?

1. I feel emotionally connected to Monteith Hall.
2. People in Monteith Hall welcome and include me in activities.
3. I am unable to influence decision-making in Monteith Hall.*
4. I feel unable to be my whole and authentic self with people in Monteith Hall.*
5. People in Monteith Hall value me and my contributions.
6. My relationships with others in Monteith Hall are as satisfying as I want them to be.
7. I feel like an “insider” who understands how Monteith Hall works.
8. I am comfortable expressing my opinions in Monteith Hall.
9. I am treated as “less than” other residents in Monteith Hall.*
10. When interacting with people in Monteith Hall, I feel like I  truly belong.

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When building this question in your collection platform, I recommend using a matrix style question. The Belonging Barometer items were randomized on the survey, so you can put these in any order or can set them to randomize in your survey platform. The scale for the question is a 5-point scale:

• Strongly disagree
• Disagree
• Neither agree nor disagree
• Agree
• Strongly agree

There are few statements in the question that were purposefully written as negative statements to drive up reliability. Those negative statements have an asterisk (*). I personally do not set response requirements on a question like this, someone may not feel comfortable responding to parts of this question and so I would not require it.

Belonging Reporting

To move from survey data to knowing how much a resident feels belonging in Monteith Hall, we are going to calculate a composite measure or score. A score is basically one number that represents multiple data points together. When we know a tool is meant to be used as a composite measure the responses to a single statement within the tool should not be reported on. That is not how the tool was built, so using it in that way actually opens up a bunch of validity and reliability concerns. All that to say, don’t report on the percentage of residents who say they agree with the statement “I am comfortable expressing my opinions in Monteith Hall.” Instead we are going to take the responses, create a score, and figure out if a resident feels community belonging.

Response Cleaning

To calculate our score, we are going to average the responses together. Before we can do that, we will need to get the data ready for the calculation. To do this we will need to clean the data.

Code Positive Statements

When I download my raw data, it shows “Strongly agree” and the other scale answers. So my first cleaning step is to code those answers to numbers. This will help in completing the calculations. I do this in two steps, starting with coding the positive statements. Those would be those without an asterisk (*). Now how you do this will differ depending on the tool you use. I download my responses and use Google Sheets, every university I worked with used Google Suite while I worked there. When all my raw data is in Google Sheets, I use find and replace to re-code my data. To code positive statements, we will use the following find and replace steps while our positive statements columns are highlighted:

1. Find Neither agree nor disagree and replace with 3
2. Find Strongly disagree and replace with 1
3. Find Strongly agree and replace with 5
4. Find Disagree and replace with 2
5. Find Agree and replace with 4

It is critical you do them in the order listed above. If you do a different order, like doing “Agree” first, all of your responses would look like this:

• Strongly dis4
• Dis4
• Neither 4 nor dis4
• 4
• Strong 4

At that point, your data is a mess and you have to do even more cleaning work. Do them in the right order and you don’t have to worry about it!

Reverse Code Negative Statements

The second cleaning step is to reverse code responses from the negatively worded statements. Remember those statements with an asterisk (*) at the end? To reverse code them, we will use the following find and replaces while our negative statements columns are highlighted:

1. Find Neither agree nor disagree and replace with 3
2. Find Strongly disagree and replace with 5
3. Find Strongly agree and replace with 1
4. Find Disagree and replace with 4
5. Find Agree and replace with 2

Again, make sure to do them in the order listed to avoid making cleaning headaches.

Address Missing Data

Now that your statements have been coded, we need to look for missing data. Missing data would happen because a resident does not respond to one of the statements. We should be treating missing data as truly missing data, instead of a 0. That means we will drop the count of completed statements from 10 to a lower number to make sure our score calculation is correct. I personally leave the cell blank so I can use the =COUNT function in Google Sheets to count how many statements received a number score. I do that function in a column that I have added to my spreadsheet, that way I have a column that I am easily able to add into my calculations.

Calculating Belonging

To calculate our score, you are going to take the average of all responded to statements. So if we were given this response set from a resident:

Statement	Response	Coded Response
I feel emotionally connected to Monteith Hall.	Neither agree nor disagree	3
People in Monteith Hall welcome and include me in activities.	Agree	4
I am unable to influence decision-making in Monteith Hall.*	Strongly agree	1
I feel unable to be my whole and authentic self with people in Monteith Hall.*	Strongly disagree	5
People in Monteith Hall value me and my contributions.	Neither agree nor disagree	3
My relationships with others in Monteith Hall are as satisfying as I want them to be.	Agree	4
I feel like an “insider” who understands how Monteith Hall works.	Disagree	2
I am comfortable expressing my opinions in Monteith Hall.	Agree	4
I am treated as “less than” other residents in Monteith Hall.*	No response	Left blank in the spreadsheet
When interacting with people in Monteith Hall, I feel like I  truly belong.	Neither agree nor disagree	3

The math to score this response would be:

That is because the actual calculation is adding all of the coded responses together and then dividing it by the number of answered statements. Which gives us:

Or, when the math is run, a nicer way to say it is a belonging score of 3.22. The scoring of the Barometer falls into three equal groups: Exclusion (scores of 1-2.33), Ambiguity (scores of 2.34-3.66), and Belonging (scores of 3.67-5). The Belonging Barometer report shares this great graphic that visualizes the scoring well:

Based on this resident’s score, we would say this resident does not feel belonging in their residence hall.

Reporting Belonging

To move from a bunch of scores to something meaningful, we have to report on our findings. Something you will notice in these recommendations and even in the last sentence from the last section, when reporting I would encourage you to group together exclusion and ambiguity groups into a non-belonging group. This helps make a clearer story, looking at just two groups: those who feel they belong and those that do not feel belonging. However, if trying to use this data to surface where it is most important to make some changes, having the exclusion and ambiguity data broken out would be helpful to know. In those cases, you might look at where there are high levels of ambiguity to make small adjustments to get quick wins while you could look at where there are high levels of exclusion where you may need to make culture adjustments. Overall, I would look at this data as percentages of residents who feel belonging and non-belonging across your team. Here are a few ways I think this data would be most useful to slice and dice with an example of a what a finding would read like:

• All residents who feel belonging and non-belonging across your campus: 74% of residents feel belonging in their residence hall. 
• Belonging based on the number of year’s campus: 84% of our first year students feel belonging in their resident hall, while 42% of second year students feel belonging. Interestingly, 94% of students who have lived on campus for three or more years feel belonging to their residence hall.
• Belonging based on length of time in a building: 89% of our second year students who have returned to the same residence hall feel belonging there, while 42% of second year students who moved residence halls feel belonging. 
• Belonging based on demographics: 79% of our residents who are women feel belonging in their residence hall, compared to just 32% of residents who are men. 68% of all gender-expansive residents feel belonging in their residence hall.
• Belonging based on residence hall type or section of campus: 89% of residents in residence halls with communal bathrooms felt belonging compared to 26% in residence halls with private bathrooms. We noticed a different pattern with gender-expansive residents, where 95% of gender-expansive residents in residence halls with private bathrooms felt belonging compared to 10% of their counterparts in residence halls with communal bathrooms.

I would caution you in reporting on these by anything smaller than a residence hall, like a floor. Small samples can make it easy for this data to become weaponized, which is the opposite of what we want to have happen. 

Now that you have your data reported on, decision makers can begin to figure out how they may address the patterns coming up in the data. Perhaps there are listening tours that need to happen to learn more about why certain patterns exist. Perhaps this data will help support a change you have wanted to make for a while. Perhaps this data will bring up something you haven’t considered before. No matter what, when we choose to not measure something we are saying it isn’t something important enough for us to understand. Please, decide it matters whether or not your students feel belonging on your staff.

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