Research & Development

Building evidence-based tools for the autism community

Development Status

APED-Q is currently in active development. The questionnaire has been developed based on:

  • Current understanding of how autism presents across the lifespan
  • Research on autistic burnout, masking, and compensatory strategies
  • Input from autistic adults and clinicians
  • Existing validated instruments (adapted conceptually, not directly copied)

Research Goals

Validation Studies

Establish psychometric properties including reliability and validity across populations.

Normative Data

Collect population norms to contextualize individual profiles.

Clinical Utility

Assess how profiles translate to meaningful support recommendations.

Community Collaboration

Partner with autistic researchers and community members throughout development.

Theoretical Foundation

APED-Q is grounded in several key concepts from contemporary autism research:

Dimensional vs. Categorical Approaches

Rather than treating autism as a binary category, APED-Q profiles continuous dimensions of experience, acknowledging that autistic traits vary in expression and impact.

Compensatory Strategies & Masking

Informed by research on camouflaging and social compensation, APED-Q measures the effort invested in adapting to neurotypical expectations.

Autistic Burnout

Drawing on emerging research, APED-Q includes items that capture patterns associated with autistic burnout risk.

Strengths-Based Framework

Consistent with neurodiversity perspectives, APED-Q explicitly measures strengths alongside support needs.

Research Collaboration

We welcome collaboration with researchers interested in:

  • Psychometric validation studies
  • Cross-cultural adaptation
  • Clinical utility studies
  • Community-based participatory research

Contact us at research@apedq.com to discuss potential collaboration.

Future Directions

1Longitudinal tracking to monitor changes over time
2Integration with clinical assessment workflows
3Development of additional language versions
4Specialized versions for specific contexts (education, employment)
5Machine learning approaches to personalize support recommendations