Because of ableism, disabled people face unique barriers to economic mobility, accessing the safety net, obtaining housing, and more. Last year, the Urban Institute, in partnership with The Kelsey, undertook new research to understand the existing and future housing needs of people with disabilities. (Note: in this post, we use the terms “disabled people” and “people with disabilities” interchangeably to employ both people-first and identity-first language. We recognize not all members of this group identify the same way, and that language evolves.)

Two elements that made our project unique were our work with a community advisory board (CAB) composed entirely of people with disabilities and community engagement in a complex quantitative analysis of national data.

People with disabilities have historically and continue to be excluded from decisionmaking and research about them. We chose to work with a CAB of people with disabilities in this project because we believe lived experience is vital expertise and evidence that strengthens research. We believe disabled people have a rightful seat at the table when it comes to decisions or policies about them—coined “nothing about us without us” by the disability rights movement—and community engagement is an important aspect of our disability equity approach at both Urban and The Kelsey.

This innovative project offers important lessons on creating inclusive CABs of people with disabilities and using community engagement approaches for quantitative research.

How we structured the project to prioritize inclusion

Our team was committed to ensuring the CAB felt prepared to give input and respected in their recommendations, so we focused on the following:

• Relationship building. Before beginning the work, we set up one-on-one meetings with each CAB member to get to know each other and learn what kinds of research knowledge, skills, and experiences they brought. We then held a kickoff meeting in which we had a team building exercise, an introductory training on research, and time to define our roles, responsibilities, and shared expectations for our partnership.
• Reviewing the data. Over several meetings, we walked through the dataset, how to read a graph and table, and what each piece of information told us. We asked CAB members what they noticed about the data, what information they felt was missing, and how to develop our story behind the data. Through these sessions and revisions, we codeveloped our findings and key takeaways.
• Disseminating findings. A data walk is a way to share back information with people who the research is for and about and get community input to contextualize the findings. Through this project, we created the first Data Walk and Wheel, a virtual convening where CAB members copresented findings and led discussions with disability advocates, leaders, and funders. Before the event, we met with each CAB member to finalize the information they wanted to share and to prepare them for their leadership role.

How to create inclusive CABs for people with disabilities 

To ensure our approach was as inclusive as possible for people with disabilities, we implemented a few best practices with our CAB that can inform similar research efforts.

• Work with community partners to understand what supports and accommodations they need and want in a partnership. Then, revisit these as a group throughout the project and adjust as needed. As a group, we developed a list of supports and accommodations offered at each meeting, including live audio captioning; plain language use; write-ups of all training and facilitation to review before, during, and after meetings; long breaks during meetings; additional one-on-ones; and the option to miss any meeting and do a make-up, no questions asked.
• Get to know community partners to understand where their interests and strengths lie, what they’re comfortable doing, and where they would like to grow. There is opportunity and tension in meeting people where they are versus preparing people to lead. To navigate this, we drew on and learned from community partners’ strengths, instead of emphasizing what they currently didn’t do. We tried to make learning and trying something new accessible, instead of something that’s forced, by learning what skills our partners were interested in gaining and creating opportunities to try instead of requirements for participation.

How to navigate community engagement with quantitative data

It’s often more difficult to take a community-engaged approach to quantitative research because it generally involves using large datasets to answer complex questions, rather than hearing directly from people. In our work, we took a few steps to overcome these challenges:

• Define the community to engage when the dataset is national. Define the community by the most relevant aspect of the dataset and by who is most adversely affected by the policy being investigated. For example, our community was people with disabilities who had or needed access to federal housing. When recruiting CAB members, we also prioritized the experiences of people of color because they face intersections of discrimination that inequitably impact their access to housing.
• Account for the time and funding that community engaged quantitative work takes to be done ethically and effectively. To do this work in full partnership with people without research experience, projects should build in longer timelines and more funding to allow for relationship building and research trainings. For example, quantitative analysis is a teachable skill. With time and planning, quantitative projects can train their partners to participate in the analysis process or be equipped to give feedback on quantitative results.
• Reflect on how community partners are shaping the work. The products of quantitative data (visualizations, text) look very different from raw datasets. This can leave room for confusion about how community partners have shaped the work, so it’s important to clearly communicate how community partners are shaping the analysis and priorities, especially the final product.
• Communicate data into accessible products. Quantitative research is often more difficult to understand than qualitative data. The research team should consider converting data into visuals and text so everyone has an opportunity to understand and respond to the data.

The input of people with disabilities is necessary evidence in research and policymaking about issues that affect people with disabilities. Our project’s CAB shaped our research to ensure our findings were useful and meaningful to disabled people, and our project was made stronger because of our engagement with the CAB. While there were challenges with using a community engagement framework for quantitative research, our project proves that it’s not only possible, but fruitful.