All States Parties to the UN Convention on the Rights of Persons with Disabilities commit to using collected information to help assess implementation of obligations and identify barriers faced by disabled people (Article 31). This page centers disabled people's expertise and supports critical, rights-based interpretation of disability data.
Statistics and data about disability can be powerful tools for advocacy and policy change—but only if interpreted carefully, with attention to context, limitations, and the social model of disability. This page helps you read, analyze, and critique disability-related data responsibly.
Disability data is rarely straightforward. Without careful interpretation:
Undercounting occurs: Data often misses certain populations (mental health conditions, episodic disabilities, neurodivergent people, cognitive disabilities)
Context gets lost: Survey methodology, question design, and sampling affect who gets counted and how
Disparities hide: Aggregated data may obscure differences across race, gender, class, geography, and disability type
Deficit narratives persist: Data can reinforce harmful framings if not interpreted through social/rights-based lenses
Good interpretation prevents misrepresentation, stigmatization, and policy missteps.
Before drawing conclusions, ask:
Break data down by:
Aggregated data often hides the most important findings.
Numbers can show trends and patterns, but lived experience reveals meaning. Effective interpretation combines:
Data should expose barriers (access, discrimination, exclusion), not reinforce deficit narratives. Ask:
All data has limitations. Acknowledge:
| Pitfall | What to Watch For | Better Approach |
|---|---|---|
| Medical-model framing | Data describing "disability rates" without acknowledging social causes | Focus on barriers and environmental factors |
| Exclusion of non-visible disabilities | Surveys counting only physical impairments | Use comprehensive question sets that capture multiple disability types |
| Overgeneralization | Extrapolating from small or non-representative samples | Note sample characteristics and limitations |
| Ignoring intersectionality | Aggregated data hiding race, gender, class differences | Disaggregate and cross-tabulate |
| Presenting data without community voice | No disabled people involved in interpretation | Include community perspectives and review |
| Treating disability as static | Snapshot data missing episodic or fluctuating conditions | Note temporal limitations |
Use this checklist when reading disability research:
Definition: How was "disability" defined? By diagnosis, functional limitation, self-identification, or other means?
Inclusion: Who was included in the sample—and who might have been excluded?
Accessibility: Were accommodations provided (translation, accessible survey tools, etc.)?
Disaggregation: Is data broken down across relevant demographics?
Framing: Are findings contextualized? Is language deficit-based or ableist?
Participation: Were disabled people involved in design, data collection, interpretation, or review?
Benefit: How will this research benefit disabled communities?
The Washington Group on Disability Statistics provides internationally recognized tools for measuring functioning. Their approach:
Website: Washington Group on Disability Statistics
The ICF provides a comprehensive framework that considers:
This framework supports interpretation that looks beyond individual impairment to environmental and social factors.
This page centers disabled people's expertise and supports critical, rights-based interpretation of disability data. For questions or to suggest additions, see How to Contribute.