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NVivo is a qualitative data analysis (QDA) computer software package produced by QSR International. It has been designed for qualitative researchers working with very rich text-based and/or multimedia information, where deep levels of analysis on small or large volumes of data are required.

NVivo is used mostly by academic, government, health and commercial researchers across a diverse range of fields, including social sciences such as anthropology, psychology, communication, sociology, as well as fields such as forensics, tourism, criminology and marketing.

What will you achieve from this course? 

At the end of this course, you will:

  • Understand the terminology and capabilities of NVivo for Windows and how it can support data analysis
  • Be able to set up a project and ensure that all the components of your research design are represented in NVivo
  • Be able to import and organize your source material in readiness for analysis
  • Start the analysis process through reading, summarising and interpretation
  • Deepen your analysis through different coding techniques and reflection.
  • Understand the value of building effective node structures
  • Work more efficiently with classification data
  • Work with text analysis queries to explore and code textual data
  • Explore patterns with Coding queries 
  • Deepen your analysis process by linking and grouping data
  • Share your progress with others.

 

Course outline

  • An overview of NVivo
  • The design framework
  • Importing data
  • Working with variable type data such as demographics
  • Starting the analysis process
  • Setting up your own project in NVivo
  • Review of your Project Setup completed in Fundamentals of NVivo
  • Creating and Modifying node structures
  • Creating External sources to represent data ‘outside’ of your project.
  • Using text analysis tools including the Text Search and Word Frequency queries
  • Using coding queries to ask questions about the data and explore patterns
  • More about coding, linking and grouping data
  • Starting to write up key issues with evidence in data
  • Exporting project items