What are the research design and data analysis strategies used in phenomenology?
1) Phenomenology is a research design that focuses on participants' lived experiences and common phenomena. The data analysis strategies used in phenomenology include taking notes while reading, sketching reflective thinking, summarizing field notes, working with words, identifying codes, reducing codes to themes, counting frequent codes, relating categories, relating categories to literature, creating a point of view, and displaying and reporting the data.
2) Phenomenology is a research design that focuses on participants' lived experiences and common phenomena. The data analysis strategies used in phenomenology include content analysis, narrative analysis, discourse analysis, thematic analysis, grounded theory, and interpretive phenomenological analysis.
3) Phenomenology is a research design that focuses on participants' lived experiences and common phenomena. The data analysis strategies used in phenomenology include taking notes while reading, sketching reflective thinking, summarizing field notes, working with words, identifying codes, reducing codes to themes, counting frequent codes, relating categories, relating categories to literature, creating a point of view, and displaying and reporting the data. Additionally, the six main qualitative data analysis methods discussed by Warren (2020) are content analysis, narrative analysis, discourse analysis, thematic analysis, grounded theory, and interpretive phenomenological analysis.
4) Phenomenology is a research design that focuses on participants' lived experiences and common phenomena. The data analysis strategies used in phenomenology include content analysis, narrative analysis, discourse analysis, thematic analysis, grounded theory, and interpretive phenomenological analysis. Additionally, Hesse-Biber et al. (2020) discuss eleven general data analysis strategies that researchers can use.