You code files in order to collect in one place—called a code —all the content related to themes you identify.
As you explore your files, you can select content and code it to a code. For example, while working through your interviews you find that a number of participants talk about the natural environment—code each occurrence to the code Natural environment
For files—capturing any issues, comments or insights that arise.
For codes—describing the significance of the code and the patterns or ideas that emerge from the references.
; memos store information about your project such as research objectives or project progress.
Make a note about the body language or voice intonation at a particular point in a conversation.
Highlight a word or phrase that needs further definition.
Comment on some aspect of the discourse.
Text searches let you search for words or phrases in your project items.
Explore the use, context and meaning of words—are some expressions used more widely in a specific demographic?
See if an idea or topic is prevalent in your files—useful in the early stages of your project.
Automatically code words or phrases. For example, find each occurrence of solar or wind power and code them (and the selected context around them) to the code renewable energy.
Explore your data and investigate hunches as you progress through your project. You can:
Find and analyze the words or phrases in your files and codes.
Ask questions and find patterns based on your coding and check for coding consistency among team members.
Once you set up the demographic data for your research participants, you can use queries and visualizations to make comparisons. For example, you could use a matrix coding query to compare attitudes about environment and community based on gender—this matrix displays the number of coding references at each intersection and helps you to answer questions like How often did women mention community? (you can double-click in a cell to see the coded content).
If you would rather ask the question How many women talked about community? you can change the count that is displayed in the matrix—right-click and select Cell Content, then click Cases Coded, and then click the classification Person.
NVivo provides quick ways to organize your demographic data and the steps vary depending on the type of files you are working with. In general, you need to:
Make a case for each participant.
Classify the case as a person—you could also have classifications for different types of people, like students or teachers.
Assign the attributes—for example, age group and gender.
Code participants' comments to their cases.
Making maps requires and ensures a thorough knowledge and understanding of your data. Mapping can also stimulate the generation of new ideas.
Condensing your research into a restricted space (a screen or page) naturally helps you to:
Identify what's most important.
Distinguish discrete pieces of data.
Establish likely relationships or connections.
You can use maps in a variety of ways, for example:
Constructing- Sort themes to build your code hierarchy. Explore alternatives for your code structure—to see what belongs in the same tree or group.
Analyzing- Develop ideas and visualize theories.
Determining- See who or what is involved in a phenomenon or process, their relationships and possible causal pathways.
Discovering- Perceive emerging themes and connections that you might not have anticipated. Reveal any apparent problems or gaps in your data.
Explaining- Express your interpretation of the data. Use evidence to show associations. Communicate your central understanding to an audience. Build a visual narrative.. Mind map Brainstorm ideas and visualize thoughts.
NVivo Research Network - Thematic Analysis Webinar - YouTube
You can visually explore datasets in the Detail View. You can:
Hide columns to limit the amount of data you are looking at—for example, if you want to see the first column in your dataset next to the fifth column, you can hide the intervening columns.
Manually code survey responses at codes representing the themes in your data.
