Qualitative Data Management and Analysis with NVIvo (Concept & Approaches) Course

Many researchers these days collect their data through a mixture of quantitative and qualitative (open ended) responses. If you are planning to conduct interviews or focus groups for your data collection, or perhaps do participant observation during key events or meetings or you will be collecting policy papers, press articles, or Internet data from online survey tools like Survey Monkey, Fluid Surveys and Survey Wizard, blogs, Facebook or Twitter, soon or later you will be faced with piles of data and field notes you collected for analysis. Will you know how?

This course aims to provide participants with strategic understanding and applied skills in planning, conducting, and reporting the process of qualitative data analysis (QDA) in a research projects. At the end of the course, participants will be able to plan, conduct, and critically assess the quality of QDA in their own and other people’s research. Participants will also learn how to use NVivo10 in QDA.

NVivo 10 is a software package for analysis of qualitative data, such as transcripts of in-depth interviews, focus groups and field notes.

DURATION

5 days

WHO SHOULD ATTEND?

This is a general course targeting participants with basic knowledge of qualitative research required. No requirement of qualitative analysis or NVivo.

Anyone from Agriculture, Economics, Food Security and Livelihoods, Nutrition, Education, Medical or public health professionals among others who already have some basic knowledge of qualitative research, but wish to be conversant with the concepts and applications of QDA qualifies.

LEARNING OBJECTIVES

• Understand how to set up a project in NVivo
• Understand how to import, create and work with codes and nodes in NVivo
• Manage all your material conveniently in one project file, easily work with material in your own language
• Effortlessly share your work with others
• Easily manage your information and enhance your internal workflow and reporting processes
• Deliver quality outputs backed by a transparent discovery and analysis process
• Justify decision making with sound findings and evidence-based recommendations
• Revisit data easily, build up the big picture perspective

Others Details

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