Action Research and Participatory Action Research (PAR)
Action research (AR) links inquiry and action in iterative cycles. Stakeholders examine practices, implement changes, and reflect on outcomes. Participatory action research (PAR) foregrounds collaboration, power-sharing, and often social justice, positioning community or practitioner co-researchers as knowledge producers.
Core action research cycle
While models differ in labeling, a robust cycle includes:
Plan — Identify a problem or opportunity with stakeholders; review evidence; define aims and ethical safeguards.
Act — Implement a contextually feasible intervention, change, or trial practice.
Observe — Collect data on process and outcomes (field notes, interviews, artifacts, metrics as appropriate).
Reflect — Analyze findings collectively; revise understanding; decide the next cycle’s focus.
Verwandte Skills
Cycles are spiraling, not one-off: each turn refines questions and practice.
Types of action research
Participatory action research (PAR)
Emphasizes democratic participation, local knowledge, and structural critique. Common in community health, education, and international development when done ethically and non-extractively.
Community-based participatory research (CBPR)
Partnership between researchers and communities across all phases—agenda setting, design, analysis, dissemination. Governance and benefit-sharing are explicit.
Classroom / practitioner action research
Teachers or clinicians study their own practice for local improvement and professional learning. Often smaller scale; still requires rigor and ethical clarity.
Appreciative inquiry (AI)
Builds change from strengths and positive exemplars rather than deficit framing. Uses discovery, dream, design, destiny phases in organizational contexts.
Researcher positionality in AR
Facilitators may be insiders (employees, members) or outsiders (academic partners). Critical issues:
Who sets the agenda?
Who benefits from publications and grants?
How is conflict among stakeholders handled?
Reflexive journaling, transparent agreements, and rotating facilitation can reduce extractive dynamics.
Collaborative inquiry principles
Co-learning — Expertise is distributed; academic theory meets local knowledge.
Shared decision-making — Protocols for consent, data ownership, and authorship.
Action orientation — Knowledge claims tied to practical effects and next steps.
Inclusivity — Design for marginalized voices to shape analysis, not only data extraction.
Quality criteria in action research
Traditional positivist criteria fit poorly. Common alternatives include:
Outcome validity — Actions produce meaningful, context-appropriate improvement.
Process validity — Inquiry is fair, inclusive, and methodologically sound for the setting.
Democratic validity — Diverse stakeholders influence conclusions and actions.
Catalytic validity — Research energizes critical reflection and empowerment (Lather).
Dialogic validity — Truth tested through sustained dialogue and challenge within the community (Anderson & Herr).
Combine these with ethical validity (do no harm, consent, privacy) and ecological validity (fits real-world constraints).
Kemmis & McTaggart’s framework
Kemmis, McTaggart, and Nixon emphasize participatory and public qualities of AR. Cycles are not merely technical but social practices that can be:
Self-reflective — Practitioners examine their own practices.
Collaborative — Joint inquiry across roles.
Public — Knowledge is shared beyond private teams to wider communities.
They stress communicative space where arguments are tested and practice architectures (material, social, cultural-discursive arrangements) are transformed.
Member validation sessions before scaling changes.
Ethics and IRB
AR can challenge IRB templates designed for fixed protocols. Use amendment-friendly designs, document evolving consent, and clarify risks when studying workplace power or community conflict.
When to use action research
Use AR when change and knowledge must advance together, stakeholders can sustain cycles, and contextual adaptation matters more than controlled experimentation.
Use traditional experiments when randomization and treatment fidelity are paramount and participatory control is inappropriate.
Use grounded theory when the primary output is substantive theory rather than intervention cycles—though GT can inform AR data analysis.
Use this skill for PAR design, cycle planning, validity arguments, or Kemmis-style participatory framing.