Edition

Doing Qualitative Data Analysis with NVivo

by Zoltán Dörnyei

Publisher: Springer

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Introduces NVivo for qualitative and mixed-methods research
Explains how to set up and manage NVivo research projects
Shows how to import and organize text, audio, video, and images
Teaches systematic coding of qualitative data
Explains theme-based and case-based coding clearly
Introduces queries, matrices, and comparisons
Shows how to write and manage analytic memos
Explains data visualization using models, maps, and charts
Covers focus groups, social media, and web-based data
Explains AI-assisted coding and advanced NVivo tools

Title	Doing Qualitative Data Analysis with NVivo
Authors	Zoltán Dörnyei
Publisher	Springer
Edition
Number Of Pages	298

Dimitri Mortelmans is a leading scholar in qualitative research methodologies and one of the most trusted trainers in NVivo software for data analysis. With many years of experience in social science research and academic teaching, he has guided countless students, researchers, and professionals in mastering the practical skills needed to work confidently with qualitative data.

Mortelmans is recognized for his ability to translate complex analytical processes into clear, step-by-step guidance. His practical teaching style helps researchers move from raw interviews, texts, or multimedia sources to meaningful insights that strengthen the quality and impact of their research.

His book, Doing Qualitative Data Analysis with NVivo, has become a valuable resource for anyone who wants to learn how to analyze qualitative data using one of the most powerful research tools available today. It offers real examples, structured techniques, and helpful tips that make the learning process easier — especially for beginners.

Dr. Mortelmans continues to contribute to the advancement of qualitative methodology through his publications, workshops, and academic leadership. Readers of this book can feel confident that they are learning from a dedicated expert who understands both the technical and interpretive sides of data analysis — ensuring strong, credible, and insightful research results.

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