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NVivo: NVivo Desktop App

Some basic resources to get started with NVivo Qualitative Analysis Software

What is NVivo?

NVivo is a software application designed to help researchers organize, analyze, and create meaning from unstructured data such as interviews, written survey responses, research literature such as journal articles and book chapters, meeting minutes, and observational field notes. It also helps researchers to visualize their analyzed data and format it for sharing with others.

The University of Lethbridge has a site license that allows current faculty, staff, and graduate students to install NVivo on their University-owned workstations and personally owned Windows or Mac computers. The current version of NVivo available at the University is NVivo 14 (as of February 2024). To find out more about accessing NVivo, see the Install NVivo tab.

NVivo in Action

The following brief videos show how NVivo can help you analyze interview transcripts and other qualitative data.

What NVivo does: NVivo lets you identify themes in your data and gather all themes across all of your data sources together in one organized hierarchical list.

What NVivo does

Importing data: You can import text files, images, video, and audio files into your NVivo project and then organize them into folders for coding. You can also import survey data.

Importing data

Coding: You can code all of your imported data to identify common and unique themes, or use a predefined set of codes. See what you have coded within each file by turning on coding stripes or highlighting.


Cases: If your project involves categories of people or organizations or places, you may find it useful to group them into cases. Using cases can expand the kinds of queries you run against your coded data.


Memos: Memos are one way that NVivo can help you record your thoughts, ideas, questions, observations, reminders, and so on, relating to analysis of their research data.

Word frequency query: This is just one of many kinds of queries you can run in NVivo to identify patterns in your data. For example a word frequency query can help you see which issues arise most frequently in your coded data.

Word frequency query

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