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Introduction to NVivo for Qualitative Data Analysis

Member rate £492.50
Non-Member rate £985.00

Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked

*If you attended our Methods School in July/August 2023 or February 2024.

Course Dates and Times

Friday 26 July 13:00–15:00 and 15:30–18:00

Saturday 27 July 09:00–12:30 and 14:00–17:30

Marie-Hélène Paré

This course will teach you how to use NVivo for the management, coding, analysis and visualisation of qualitative data. It follows the qualitative data analysis process in sequence, and covers how to:

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

The course does not cover how to analyse qualitative data in NVivo based on specific analytical methods such as thematic analysis, grounded theory, or content analysis. This is covered in the course Advanced Qualitative Data Analysis.

Tasks for ECTS Credits

1 credit (pass/fail grade). Attend at least 90% of course hours, participate fully in in-class activities, and carry out the necessary reading and/or other work prior to, and after, class.

Instructor Bio

Marie-Hélène is a highly regarded methodologist who has NVivo Certified Platinum Trainer status. She has shared her expertise in qualitative data analysis with over 60 universities and research centres around the world, including Qatar and Iran. Since 2009, Marie-Hélène has been teaching introductory and advanced courses in qualitative data analysis at the ECPR Methods School. Her areas of methodological interest include qualitative evidence synthesis, decolonising epistemology, and participatory methodologies. Marie-Hélène is dedicated to advancing the field of qualitative data analysis and sharing her knowledge with others.


NVivo is 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
  • analytic induction
  • qualitative research synthesis.

This course aims to give you 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
  • work with multimedia
  • manage a literature review
  • autocode and code data inductively
  • generate hypotheses
  • seek patterns and discover relationships
  • present qualitative findings.

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 an 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

We cover functionalities required to prepare and conduct qualitative analysis. Since a large proportion of social research gathers qualitative data, as well as variables, 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

I propose different graphic displays to effectively communicate research findings. We first discuss the rationales for choosing certain displays over 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.

This is a bring-your-laptop course using NVivo 12. Download the NVivo 14-day free trial for Windows and Mac.

You must ensure that NVivo works well on your machine regardless of the OS. No technical assistance is provided at the Winter School.

Find more installation instructions in the Software and Hardware sections below.

Day Topic Details
Friday afternoon: 13:00–​​​​​​​15:00 and 15:30–​​​​​​​18:00 = 4.5 hours IDEM
  1. Introducing NVivo: interface and menus
  2. Importing and organising data
  3. Managing a literature review
  4. Exploring textual data
  5. Linking one’s project to external information
  6. Memoing’s one research
Saturday morning: 9:00–​​​​​​​10:30 & 11:00–​​​​​​​12:30 = 3 hours IDEM
  1. Autocode structured data
  2. Generate codes inductively
  3. Manage a coding scheme
  4. Generate / falsify hypotheses
  5. Visualise the coding process
  6. Work with cases and variables
Saturday afternoon: 14:00–​​​​​​​15:30 & 16:00–​​​​​​​17:30 = 3 hours IDEM
  1. Using search tools
  2. Clustering items in sets
  3. Seeking patterns using queries
  4. Presenting findings with visualisations
  5. Generating summary reports
  6. Exporting data and findings out of NVivo
Day Readings
Saturday Morning



Cases and variables: Organize your demographic data; create case classifications; create cases manually; create cases automatically; classify cases.

Saturday Afternoon



Preparing reports: Strategies for team work; preparing for final wrap-up.





The readings on this course consist of NVivo Online Help. See the links below for NVivo for Windows and Mac

Software Requirements

Software requirements

NVivo 12 Pro for Windows / NVivo 12 for Mac.

This is a bring-your-laptop course. You must run NVivo 12 Pro for Windows or NVivo 12 for Mac on your laptop.

Download the 14-day free trial for Windows and Mac

It is your responsibility to ensure that NVivo works well on your laptop before you arrive in Bamberg. Once NVivo is installed on your laptop, verify that it works properly by following the instructions below.

  1. Launch NVivo
  2. On the Start screen (Windows version), in the New section, click Sample Project
  3. On the Welcome to NVivo for Mac screen (Mac version), click Create a copy of the sample project
  4. NVivo opens the sample data project
  5. If you can’t open the sample project, contact QSR International by submitting a support request form online (see section Contact Us Online at the bottom of the page).

Hardware requirements

NVivo 12 Pro for Windows





1.2 GHz single-core processor (32-bit) 1.4 GHz single-core processor (64-bit)

2.0 GHz dual-core processor or faster


2 GB RAM or more

4 GB RAM or more


1024 x 768 screen resolution

1680 x 1050 screen resolution

Operating system

Microsoft Windows 7 SP1

Microsoft Windows 7 SP1 or later

Hard disk

5 GB of available hard-disk space

8 GB of available hard-disk space


NVivo 12 for Mac





Intel Core 2 Duo, Core i3, Core i5, Core i7, or Xeon processor

Intel Core i5, Core i7, or Xeon processor


4 GB RAM or more

8 GB RAM or more


1080 x 800 screen resolution

1440 x 900 screen resolution

Operating system

Mac OS X 10.11 (El Capitan)

Mac OS X 10.11 (El Capitan)

Hard disk

3GB of available disk space

4GB SSD of available disk space

Hardware Requirements

See software requirements



Bazeley, P., & Jackson, K. (2013). Qualitative Data Analysis with NVivo (2nd ed.). London: Sage.

Empirical articles

Andrew, S., Salamonson, Y., & Halcomb, E. J. (2008). Integrating mixed methods data analysis using NVivo: An example examining attrition and persistence of nursing students. International Journal of Multiple Research Approaches, 2(1), 36-43.

Auld, G. W., Diker, A., Bock, M. A., Boushey, C., J, Bruhn, C. M., Cluskey, M., . . . Zaghloul, S. (2007). Development of a Decision Tree to Determine Appropriateness of NVivo in Analyzing Qualitative Data Sets. Journal of Nutrition Education and Behavior, 39(1), 37-47.

Bazeley, P., & Jackson, K. (2013). Perspectives: Qualitative computing and NVivo. Qualitative Data Analysis with NVivo (2nd ed., pp. 1-22). London: Sage. Retrieved from

Blaney, J., Filer, K., & Lyon, J. (2014). Assessing High Impact Practices Using NVivo: An Automated Approach to Analyzing Student Reflections for Program Improvement. Research & Practice in Assessment, 9.

Brandão, C., & Miguez, J. (2017). Using NVivo to assess a program of goal-corrected empathic attunement skills: a case study in the context of higher education. Universal Access in the Information Society, 16(4), 863-876

Bringer, J. D., Johnston, L. H., & Brackenridge, C. H. (2004). Maximising transparency in a doctoral thesis: the complexity of writing about the use of QSR* NVIVO within grounded theory study. Qualitative Research, 4(2), 247-265.

Bringer, J. D., Johnston, L. H., & Brackenridge, C. H. (2006). Using Computer-Assisted Qualitative Data Analysis Software to Develop a Grounded Theory Project. Field Methods, 18(3), 245-266.

Davidson, J. (2012). The Journal Project: Qualitative Computing and the Technology/Aesthetics Divide in Qualitative Research. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 13(2), Art. 15.

Deakin, H., Wakefield, K., & Gregorius, S. (2012). An Exploration of Peer-to-Peer Teaching and Learning at Postgraduate Level: The Experience of Two Student-Led NVivo Workshops. Journal of Geography in Higher Education, 36(4), 603.

Edwards-Jones, A. (2014). Qualitative data analysis with NVIVO. Journal of Education for Teaching, 40(2), 193.

Fàbregues, S., & Paré, M.-H. (2018). Appraising the quality of mixed methods research in nursing: A qualitative case study of nurse researchers’ views. Nursing Inquiry, e12247. doi: 10.1111/nin.12247.

Fàbregues, S., Paré, M.-H., & Meneses, J. (2018). Operationalizing and Conceptualizing Quality in Mixed Methods Research: A Multiple Case Study of the Disciplines of Education, Nursing, Psychology, and Sociology. Journal of Mixed Methods Research. doi: 10.1177/1558689817751774.

Hoover, R. S., & Koerber, A. L. (2011). Using NVivo to Answer the Challenges of Qualitative Research in Professional Communication: Benefits and Best Practices Tutorial. IEEE Transactions on Professional Communication, 54(1), 68.

Houghton, C., Murphy, K., Meehan, B., Thomas, J., Brooker, D., & Casey, D. (2016). From screening to synthesis: using NVivo to enhance transparency in qualitative evidence synthesis. Journal of Clinical Nursing, 26, 873–881. doi: 10.1111/jocn.13443.

Hutchison, A. J., Halley Johnston, L., & Breckon, J. D. (2010). Using QSR-NVivo to facilitate the development of a grounded theory project: an account of a worked example. International Journal of Social Research Methodology, 13(4), 283-202.

Johnston, L. (2006). Software and Method: Reflections on Teaching and Using QSR NVivo in Doctoral Research. International Journal of Social Research Methodology, 9(5), 379-391. doi: 10.1080/13645570600659433.

Leech, N. L., & Onwuegbuzie, A. (2011). Beyond Constant Comparison Qualitative Data Analysis: Using NVivo. School Psychology Quarterly, 26(1), 70-84.

Min, M., Anderson, J. A., & Chen, M. (2017). What Do We Know About Full-Service Community Schools? Integrative Research Review With NVivo. School Community Journal, 27(1), 29-54.

Phillips, M., & Lu, J. (2018). A quick look at NVivo. Journal of Electronic Resources Librarianship, 30(2), 104-106.

Pudaruth, S., Moheeputh, S., Permessur, N., & Chamroo, A. (2018). Sentiment Analysis from Facebook Comments using Automatic Coding in NVivo 11. ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal, 7(1), 41-48.

Rich, M., & Patashnick, J. (2011). Narrative research with audiovisual data: Video Intervention/Prevention Assessment (VIA) and NVivo. International Journal of Social Research Methodology, 5(3), 245-261.

Robins, C. S., & Eisen, K. (2017). Strategies for the Effective Use of NVivo in a Large-Scale Study: Qualitative Analysis and the Repeal of Don't Ask, Don't Tell. Qualitative Inquiry, 23(10), 768-778.

Siccama, C., & Penna, S. (2008). Enhancing Validity of a Qualitative Dissertation Research Study by Using NVIVO. Qualitative Research Journal, 8(2), 91-103.

Wainwright, M., & Russell, A. (2010). Using NVivo Audio-Coding: Practical, Sensorial and Epistemological Considerations. Social Research Update, 60(1), 1-4.

Wiltshier, F. (2011). Researching With NVivo 8. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12(1), Art. 23.

Wong, L. P. (2008). Data analysis in qualitative research: a brief guide to using NVivo. Malaysian Family Physician 2008, 3(1), 14-20.

Zamawe, F. C. (2015). The Implication of Using NVivo Software in Qualitative Data Analysis: Evidence-Based Reflections. Malawi medical journal : the journal of Medical Association of Malawi, 27(1), 13-15.

Zapata-Sepúlveda, P., López-Sánchez, F., & Sánchez-Gómez, M. C. (2012). Content analysis research method with Nvivo-6 software in a PhD thesis: an approach to the long-term psychological effects on Chilean ex-prisoners survivors of experiences of torture and imprisonment. Quality & Quantity, 46(1), 379-390.

Recommended Courses to Cover Before this One

Summer School

Ethnographic and Other Field Research Methods: Introduction
Ethnographic and Other Field Research Methods: Advanced
Analysing Political Discourse I: Theories, Concepts and Research Designs
Analysing Political Discourse II: Analyses and Applications

Recommended Courses to Cover After this One

Summer School

Qualitative Data Analysis: Concepts and Approaches
Expert Interviews for Qualitative Data Generation

Winter School

Focus Groups – From Qualitative Data Generation to Analysis