Introduction to NVivo for Qualitative Data Analysis

NVivo is a software programme for qualitative data analysis. It is a powerful platform that supports text, multimedia, pictures, PDFs, open-ended surveys from Excel and Survey Monkey, reference libraries, webpages, social media data from Facebook, Twitter, LinkedIn, and YouTube, notes from Evernote and OneNote, and emails from Outlook.

NVivo supports a range of inductive and deductive methods to qualitative analysis such as

• thematic and content analysis
• within and cross-case analysis
• discourse
• conversational and narrative analysis
• grounded theory
• analytical induction
• qualitative research synthesis.

You will gain the knowledge and skills to use the basic and advanced features of NVivo in your own research.

The content is spread over four modules and will teach you how to:

• set up a project and organise data
• manage a literature review
• code and analyse data
• present qualitative findings using graphic displays.

Module 1

Data Management

I open with notions of qualitative research designs and their application in a NVivo project.  We review how data can be organised in comparative and non-comparative designs, coding approaches developed, and types of analyses conducted.

We then move in NVivo and import and organise a range of qualitative data. We learn the key features that support a literature review so sources can be annotated and cross-referenced to highlight a line of arguments and connections across sources.

We turn our attention to the transcribing possibilities of NVivo, starting with transcribing media recordings in full or working only with sound and video sequences. I will explain how to work with still images. I explain how you can work directly on pictures or generate a log to associate comments with specific picture regions.

We create externals that link a NVivo project to outside information, and memos where the analytic process is recorded. Module 1 concludes with lexical queries which search for frequency, occurrence, and context of keywords in textual data. We analyse the outputs using word clouds, dendograms, and wordtrees.

Module 2

Data Coding          

I introduce techniques to autocode and code data inductively in NVivo. We start by autocoding questions from structured interviews, so the responses of each question are gathered in one node.

Such data sorting – known as broad-brush coding – is very useful when you want to examine everything that was said about a question or a theme across a dataset without having to open every source of a project.

We move on to inductive coding and learn tools to code data manually. I will discuss and simplify key notions underlying the coding process such as coding unit, semantic exclusiveness, semantic exhaustiveness, and coding cooccurrence.

I introduce the use of relationship nodes to formalise relationships between codes when working towards hypothesis generation or falsification.

Module 2 concludes with visualisations that support the coding process from inception to end.  

Module 3

Data Analysis

Module 3 covers functionalities required to prepare and conduct qualitative analysis. Since a large proportion of social research gathers qualitative data, as well as variables, so comparison can be made across cases and sub-sets of cases.

First, we look at how to create cases from interview data, to import variables from Excel, and merge these to the cases. We extend our use of cases to policy documents, where comparisons are made on document data, and not cases of individuals. In both instances, we use the functionality of source and node classifications to define type of sources and cases in the dataset.

With the cases created, we turn to the NVivo search tools that efficiently retrieve cases matching a specific search string. This allows us to create sets of cases and documents for comparative analysis.  

Module 4

Data Visualisation

Module 4 proposes different graphic displays to effectively communicate research findings. We first discuss the rationales for choosing certain displaysover others. We learn to generate maps, charts, diagrams, and dendograms.

Building a solid audit trail to back up results and substantiate one’s claims, we learn how to export qualitative findings out of NVivo, to use in Word, Excel, and PowerPoint. I will also cover the usefulness of generating nodes summary reports, which provide detailed synthesis of the scope of a node in a project.

If you have colleagues who don’t use NVivo, I will show you how to export project data in mini websites using HTML files.

No prerequisite knowledge of NVivo required.  Knowledge of qualitative research is necessary.