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 to set up a project, organise, and classify data; manage a literature review; code and analyse text, multimedia and internet data; seek patterns and discover relationships; and report qualitative findings using visualisations. Details of the four modules is presented below.

Module 1 Data Management
The course opens with an introduction on CAQDAS in general, and on NVivo in particular. We move in NVivo, create a project, import, organise, and classify qualitative data. We learn the features to manage a literature review such as annotations, cross-references, and memo links for easy access and retrieval. Our attention turns to handling multimedia data starting with the generation of verbatim transcripts and then the creation of video and audio file summaries. When working with pictures we look at the possibility to work with a log or to comment on only regions of a picture. We then touch on the use of externals, which link a 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 with 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 piece of interview where the question was asked. Key formatting tips in Word accompany this topic.

We move on with manual coding and learn the different techniques to code data 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 as descriptive information in the form of variables so comparisons can be made across cases, settings, and contexts, we first look at the procedures to create classifications, cases, and assign variables to cases from Excel. We then turn to the NVivo Find bar tool that efficiently retrieves cases that match a specific sociodemographic profile. This allows us to create sets of respondents for cross-case comparative analysis. 

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 a 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 find out 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, so these can be used in Word, Excel, and PowerPoint. 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 a particular 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 11 Pro for Windows

This is a bring-your-laptop course for NVivo 11 Pro for Windows. You can download the 14-day free trial here. This course is unsuitable for NVivo 11 for Mac as this version is incomplete compared to Windows. You can run NVivo 11 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 assistant, ECPR staff, or CEU IT services.  Please see the software installation instructions in the section Software and Hardware below.