手法を比較

	Delphi Method	アクションリサーチ	混合研究法
分野	質的手法	質的研究	質的手法
系統	Process / pipeline	Process / pipeline	Process / pipeline
提唱年	1963	1946	—
提唱者	Norman Dalkey & Olaf Helmer (RAND Corporation)	Kurt Lewin; expanded by Kemmis, McTaggart, Reason & Bradbury	—
種類	Structured iterative expert-elicitation process	Method	Research design framework
原典	Dalkey, N. & Helmer, O. (1963). An Experimental Application of the Delphi Method to the Use of Experts. Management Science, 9(3), 458-467.	Lewin, K. (1946). Action research and minority problems. Journal of Social Issues, 2(4), 34–46.	Creswell, J.W. & Plano Clark, V.L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379
別名	Delphi Yöntemi, Delphi technique, expert consensus method	Participatory Action Research, PAR, Collaborative Inquiry	Karma Yöntem Araştırması (Mixed Methods), multi-method research, triangulation design
関連	5	1	4
概要	The Delphi method is a structured, iterative survey technique developed by Norman Dalkey and Olaf Helmer at the RAND Corporation in 1963 for eliciting and converging expert opinion on complex topics where empirical data are unavailable or insufficient. It collects independent judgements from a geographically dispersed expert panel over multiple anonymous rounds, feeding aggregated results back to participants after each round so they can revise their views in light of the group's collective position.	Action research is a collaborative research methodology in which researchers work with practitioners and community members to investigate a problem, implement change, and evaluate outcomes, cycling through reflection, action, and learning. Developed by Kurt Lewin (1946), action research bridges research and practice, aiming simultaneously to produce knowledge and practical improvement.	Mixed methods research is a systematic research design in which quantitative and qualitative data are collected and analysed within a single study. Formalised by Creswell and Plano Clark (2003, 3rd ed. 2018), it offers three principal design variants — concurrent, sequential, and transformative — and strengthens findings through triangulation across both data strands.