方法对比

	行动研究	内容分析	混合方法研究
领域	质性研究	质性	质性
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1946	Systematised through Krippendorff's methodology work; 4th edition 2018	—
提出者	Kurt Lewin; expanded by Kemmis, McTaggart, Reason & Bradbury	Klaus Krippendorff (systematic formulation); roots in early 20th-century communications research	—
类型	Method	Qualitative / mixed-method research technique	Research design framework
开创性文献	Lewin, K. (1946). Action research and minority problems. Journal of Social Issues, 2(4), 34–46.	Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage. ISBN: 978-1506395661	Creswell, J.W. & Plano Clark, V.L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379
别名	Participatory Action Research, PAR, Collaborative Inquiry	İçerik Analizi, systematic content coding, quantitative content analysis	Karma Yöntem Araştırması (Mixed Methods), multi-method research, triangulation design
相关	1	5	4
摘要	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.	Content analysis is a systematic research technique for reducing text, visual, or media material into coded categories so that patterns can be counted, compared, and interpreted. Formalised by Klaus Krippendorff in his widely cited methodology textbook (latest edition 2018), the method sits at the boundary of qualitative and quantitative inquiry: it imposes structured, replicable coding on inherently meaning-laden material.	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.