The disabled population in the US is diverse and multifaceted. But most data collection methods and sources—particularly major national surveys—fall short of capturing the breadth of the disabled population’s experiences, posing challenges for researchers interested in using data and evidence to improve the lives of disabled people.

Despite the challenges, better research and data on disabled people’s experiences are possible. Below we share lessons for how to responsibly use existing data to conduct research about people with disabilities. The lessons are based on our recent analysis, conducted in partnership with The Kelsey, exploring the housing needs and barriers faced by disabled people in the US.

Define who you’re talking about

No available data source fully captures the diversity of disabled people’s experiences. Most major federal surveys collect information about disabilities, but every survey differs in how it collects data and who it captures. Since 2008, most federal surveys—including the Current Population Survey (CPS), the American Housing Survey, and the American Community Survey, or ACS—have collected information about disability through six questions focused on conditions that may hinder activities of daily living, such as dressing, bathing, and doing errands. The Census Bureau developed this framework to try to move away from using specific conditions or diagnoses to define disability and to reflect a wider group of people with disabilities.

Though this framework improves on past approaches, it doesn’t capture everyone with a disability. Because these questions focus on activities of daily living, their ability to capture people with neurological disabilities, mental illnesses, and chronic illnesses is limited. A recent analysis found that this six-question framework failed to identify almost 20 percent of people with self-identified, enduring disabilities in a national sample of working-age adults.

In addition, disability isn’t a static or binary condition. It exists on a continuum and can change over time, and a survey respondent’s answers to disability-related questions might differ depending on how and when they’re surveyed. In our analysis based on the 2021 CPS, we found that a significant number of respondents who selected “no” for all six disability questions also reported a “disability or illness” as a reason for not working elsewhere in the survey. To capture the broadest group of disabled people, our study population combined this group with those who responded “yes” to at least one disability question. But we know this definition likely still misses some people with disabilities.

Know your data source’s limits 

Even with a consistent question framework, different data sources can give different estimates of the disabled population. In our analysis, we estimated that nearly 39 million people ages 15 and older in the US had a disability in 2021. But the Centers for Disease Control and Prevention estimated that 61 million American adults were disabled in 2016, and the 2019 ACS indicated that 41 million noninstitutionalized American adults were disabled. These discrepancies are likely the result of the surveys’ different sampling methods, populations, and intentions. This is why it’s essential for researchers to understand the limitations of their data sources when determining how to use data to research the disabled population.

For example, the CPS only surveys people in housing units. This approach leaves out several significant groups, including people experiencing homelessness or living in institutions such as correctional or nursing facilities. These missing groups are disproportionately likely to be disabled—the 2020 ACS estimated that 53 percent of people living in institutions have a disability. Omitting these groups from the CPS could significantly limit efforts to estimate the disabled population as a whole.

How can we improve research?

Quality research about disabled experiences is key for improving the lives of people with disabilities. Researchers should take the following steps to ensure such research is meaningful and useful:

• Involve disabled people in the research process. Partnering with disabled people when examining challenges facing this group is essential. For our analysis of housing barriers and needs among the disabled population, our research team included people with and without disabilities. We partnered with a community advisory board (CAB), a diverse group of disabled people with a range of personal and professional expertise. The CAB members interrogated our analysis, brought their lived experiences to our conversations, and led much of the work.
• Be clear about the limitations of your data sources. Though data analysis can uncover useful information, it’s important to note the limitations of any data sources used in research. In both the fact sheet and methodology appendix for our analysis, we specified the parameters of our study population.
• Ask disabled people to self-identify. Many CAB members suggested that data collection could be strengthened by allowing people to self-identify as disabled in surveys. This would capture a different slice of the disabled population and could be asked in addition to questions about specific challenges.
• Explore modeling techniques to fill knowledge gaps. Microsimulation modeling can simulate the characteristics of individuals in a population. These techniques allow for more nuanced analyses of resource needs or gaps in program participation and allow researchers to forecast how those gaps might change based on different factors. For example, microsimulation modeling could project the housing needs of the US disabled population.

Data and evidence are key tools for communities, policymakers, and other practitioners working to improve the lives of disabled people, and researchers are critical in ensuring data are meaningful and representative. By thoughtfully collaborating with disabled people, understanding and communicating the limitations of data sources, and exploring new research and data collection methods, researchers can produce stronger, more inclusive evidence about people with disabilities.