Introduction to NVivo for Qualitative Data Analysis

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NVivo is software programme for qualitative data analysis. It is a powerful platform that supports text, multimedia, pictures, and PDFs; open-ended surveys from Excel and Survey Monkey; bibliographic meta-data from reference manager software; social media data from Facebook, Twitter, LinkedIn, YouTube as well as webpages; notes taken with Evernote and OneNote; and emails of 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, and qualitative evidence synthesis. The objective of this course is to provide participants with knowledge and skills to use the basic and advanced features of NVivo in their own research. The course content is spread over four modules and includes setting up a project; organising and classifying data; managing a literature review; coding and analysing text, multimedia and internet data; seeking patterns and discovering relationships; and reporting qualitative findings using visualisations. Details of the four modules is presented below.

Module 1 Data Management

The course opens with an introduction of the NVivo interface, its structure and underlying logic. We create an NVivo project, import, organise, and classify data. We learn to manage a literature review using annotations, cross-references, and memo links for easy access and retrieval. Our attention then turns to handling multimedia data starting with the generation of verbatim transcripts, video summaries and picture logs. We then use externals to link an NVivo project to outside information, as well as memos where the analytic process is recorded. Module 1 concludes with lexical queries, which search for frequency and context of keywords in textual data. We analyse the outputs using word clouds, dendograms, and word trees.

Module 2: Data Coding          

Module 2 introduces the different techniques to automatically and manually code qualitative data in NVivo. We start by autocoding data from structured interviews, which allows to sort large sections of data in thematic sections. Such data sorting - known as ‘broad-brush’ coding - is very useful when one wants to examine everything that was said about a specific question or theme across a dataset, without having to open every single piece of interview where the question was asked. Formatting tips in Word accompany this topic.

We move on with manual coding and learn the different techniques to code data inductively; that is, using a bottom-up approach. Key notions underlying the coding process such as coding unit, semantic exclusiveness, and semantic exhaustiveness, are exemplified with the material at hand. The use of relationship nodes is tried out when one wants to formalise associations between the codes for hypothesis generation and/or falsification. Module 2 concludes with visualisations that map the coding process and compare coding across sources and cases.

Module 3: Data Analysis

Module 3 covers the range of functionalities to prepare and conduct qualitative analysis. Since social research frequently gather qualitative data as well variable data, so comparisons can be made across cases, settings and contexts, we look at the procedures to create case classifications and assign variables to cases. We then turn to the Search Folder to efficiently retrieves cases that match a specific sociodemographic profile. This allows us to create sets to be used for the cross-case comparation. 

We then move on with coding-based queries which retrieve data based on boolean operators that search for data overlap, inclusion, proximity, or exclusion. We start with coding query that searches for data coded at some nodes but only when mentioned by cases that match specific attributes. For cross-case analysis, we run a matrix query which cross-tabulates cases with codes, and we interpret the results using different outputs: coding density, case number, and relative percentage. Our interpretation is recorded in memos and is linked back to theory. Module 3 concludes with running group query to explore association between coded items across a dataset.  

Module 4: Data Visualisation

Module 4 proposes different graphic displays to effectively communicate one’s research findings. We first discuss the rationale for choosing certain displays against others. We learn to generate maps, charts, diagrams, and dendograms. Moving on to building a solid audit trail to back up results and substantiate one’s claims, we learn how to export qualitative findings out of NVivo. The usefulness of generating node summary reports, which provide a detailed synthesis of the scope of a node in a project, is also covered. When working with colleagues who don’t use NVivo, the possibility to export project data in mini websites using HTML files is presented.

Module 4 concludes with the ABC of coordinating team work with an emphasis on the golden rules for successful data management, splitting and merging project files in a master project, and the measurement of intercoder reliability between coders.

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

This course uses NVivo 12 Pro for Windows

This is a bring-your-laptop course for NVivo 12 Pro for Windows. You can download the 14-day free trial here. This course is unsuitable for NVivo for Mac as this version is incomplete compared to Windows. You can run NVivo 12 Pro for Windows on a Mac using Apple Boot Camp or Parallels if, and only, your Mac meets the system requirements here. You must ensure that NVivo works well on your machine regardless of the OS used as no technical assistance will be provided at the Summer School by the instructor, teaching assistants, ECPR staff, or CEU IT services.  Please see the software installation instructions in the section Software and hardware requirements below.