Modes Of Thinking For Qualitative Data Analysis
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Modes of Thinking for Qualitative Data Analysis
Melissa Freeman describes five modes of thinking or analytic strategies for qualitative research to demonstrate how each puts into action a distinct theoretical orientation and goal for research.
Modes of Thinking for Qualitative Data Analysis
Modes of Thinking for Qualitative Data Analysis offers a creative and comparative account of the conceptual and philosophical underpinnings of six prominent analytical movements used by interdisciplinary qualitative researchers: Categorical thinking, narrative thinking, dialectical thinking, poetic thinking, diffractive thinking, and decolonial thinking. Each “mode of thinking” engages in a particular process for analytical sense-making. Every chapter describes core characteristics of the focal analytical movement along with examples to illustrate what that approach might look like in practice. By presenting these analytical movements in the space of a single text, the author not only highlights their unique contributions to qualitative analysis but also creates a structure from which to understand their strengths and challenges, as well as the kind of outcomes and realities each participates in shaping. This book is a thought-provoking resource for experienced and novice interdisciplinary qualitative researchers seeking to deepen their understanding of the variety of ways qualitative analysis is, and could be, theorized and practiced.
Qualitative Data Analysis with ATLAS.ti
Now updated to cover latest versions and featuring instructions for both Mac and Windows users, this book is still the go-to source of support for getting to grips with qualitative data analysis using ATLAS.ti. With over twenty years of experience using, developing, and teaching ATLAS.ti, Susanne Friese is the perfect data analysis tour guide. Aware of common challenges and sticking points, she eases readers from readying and organizing data into coding and querying it, providing not only tips on how to prepare for analysis, but also the tools and technical know-how needed to observe, examine, and discuss data. Placing quick software ‘skills training’ tutorials alongside different stages of the data analysis process, she gives readers the opportunity to integrate software training with their actual analysis.