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Member rate £492.50
Non-Member rate £985.00
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*If you attended our Methods School in Summer/Winter 2024.
Friday 26 February: 13:00-15:00 and 15:30-17:00
Saturday 27 February: 09:30-11:30 and 12:30-14:30
7.5 hours over two days
This course is designed for participants who plan to use NVivo for the management, coding, analysis and visualisation of qualitative data. The course’s content is spread over four modules and includes setting up an NVivo project and organising data; coding data, working with case and attributes; seeking patterns and identifying relationships across data, and presenting findings using graphic displays. The course is entirely hands-on and uses sample data to learn NVivo’s basics and advanced features. The course does not cover the methods to analyse qualitative data (i.e. thematic analysis, grounded theory, qualitative content analysis, etc.).
Please read the full course outline.
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 a software programme for qualitative data analysis. It is a powerful platform that supports text, PDF, video, audio, and picture data; open-ended surveys from Excel and Survey Monkey; bibliographic libraries from Endnote, Zotero, Mendeley and RefWorks; Internet data from web pages as well as social media from Facebook, Twitter, LinkedIn, YouTube; and notes taken with Evernote and OneNote. NVivo supports a range of inductive, deductive and abductive methods to analyse qualitative data such as thematic and content analysis, single case and cross-case analysis, discourse, conversational and narrative analysis, grounded theory (different schools of), analytical induction, and qualitative research synthesis. The objective of this course is to provide participants with the knowledge and skills to use the basic and some of the advanced features of NVivo in their research. Participants will learn to apply their research design in NVivo, set up a project and organise data, code a range of data type, work with cases and attributes, seek patterns and identify relationships, and generate graphic displays to present findings. The course solely focuses on the functionalities of NVivo and does not cover methods to analyse qualitative. The course is structured around four modules which are detailed below.
Module 1 Data Management
The course opens with guidelines on how to apply one's research design in NVivo: type(s) of data collected, unit of analysis, cases and variables (when applicable), coding approach, and choice of analytic method. We then create an NVivo project, import and organise interview and focus group transcripts, audio and video recordings, PDF from literature review, survey data, Tweets and Facebook posts, and bibliographical meta-data. When using NVivo for literature review, we learn to cross-reference sources that support or contradict a given line of argument, and learn how to show connections between sources and authors. Our attention then turns to the transcription possibilities of NVivo, starting with transcribing audio and video recordings in-full, or working only with sequences of data. Looking at picture data, we explore the possibility to comment on a whole picture or only regions of it. We move on to create externals that link an NVivo project to outside information, as well as creating memos to record the analytic process. Module 1 concludes with lexical queries which search for the most cited words, as well as the occurrence and context in which keywords are mentioned across textual sources. We analyse the outputs in word clouds, dendograms, and wordtrees.
Module 2 Data Coding
Module 2 presents the different techniques to automatically and manually code data 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 one wants to examine everything that has been said about a question or theme across a dataset. But the analytic task of coding really starts with the creation of categories which may derive from theory or be inductively created from the data. Accordingly, we create a coding scheme where different types of codes co-exist and which capture ideas in text data, video transcripts, audio recordings, pictures, and social media data. The use of relationship nodes is introduced to formalise relationships between codes when working towards hypothesis generation or falsification. Module 2 concludes with mapping the coding process in models and graphs.
Module 3 Data Analysis
Module 3 is concerned with the preparation and the conduct of qualitative data analysis through seeking patterns and identifying relationships across the data. We first set up the case and variable structure from Excel which results in the cases being listed in a case structure with their variables assigned. With the case list set-up, we turn to the NVivo search tool which is time-saver functionality when one wants to quickly locate project items based on their name, creation date, or attribute values. We search for cases that match specific sociodemographics and grouped these in sets so we can later compare what, and how much, was said for specific codes across sets of respondents. We then move on with coding-based queries which retrieve data based on patterns of codes co-occurrence, proximity, sequence, or exclusion. We first run coding queries that search for data coded at some nodes but only when mentioned by respondents of a given profile. For cross-case analysis, we run matrix queries which cross-tabulate cases with codes and we interpret the results using different numerical readings: coding density, number of cases, relative percentage, etc. Our interpretation is recorded in memos and is linked back to theory. Module 3 concludes with running group query to find out association between items across the data.
Module 4 Data Visualisation
Module 4 proposes different graphic displays to effectively communicate one’s research findings. We first discuss the rationales for choosing certain displays against others. We learn how to generate models, charts, graphs, dendograms, and maps. Moving on to building a solid audit trail to back up results and substantiate one’s claims, we learn how to export results out of NVivo so these can be used in Word, Excel, and PowerPoint. The usefulness of generating nodes summary reports, which provide 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.
This is an introductory course on NVivo with no prerequisite knowledge. Knowledge of qualitative research is necessary. This course is solely taught with NVivo 10 for Windows. Do not come to the course with NVivo for Mac. See the section Software requirements below for software installation instructions.
Day | Topic | Details |
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Friday | Data management Data coding |
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Saturday morning | Data coding Data analysis |
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Saturday afternoon | Data analysis Data visualisation |
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Day | Readings |
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Note | This course is based on the book by Bazeley & Jackson 2013. Qualitative Data Analysis with NVivo (2nd ed.). You must obtain the book before the Winter School as the University of Bamberg library does not hold a copy |
Friday | Data Management 1. Format interview and FG data: chapter 3 pp. 59-61 2. Download data from internet: chapter 7 pp. 173-177 3. Set up a project and organise data: chapter 2 pp. 24-34; 45-46; chapter 3 internals pp. 61-67; chapter 9 survey pp. 195-204; social media pp. 209-216; chapter 11 framework matrices pp. 259-260 4. Transcribe multimedia data: chapter 3 pp. 56-59; chapter 7 pp. 154-177 5. Link to external data and memos: chapter 2 pp. 36-38; chapter 3 pp. internals 62-63; chapter 4 p. 87 6. Import literature review sources: chapter 8 pp. 178-194 |
Saturday morning | Data Coding 1. Autocode structured data: chapter 5 pp. 108-110; chapter 9 survey : 207- 208 2. Create codes and coding: chapter 4 pp. 68-94 3. Manage a coding scheme: chapter 5 pp. 95-106; 117-119 4. Build relationship nodes: chapter 10 pp. 230-234 5. Insert annotations and link data: chapter 2 pp. 34-45 6. Create case nodes and classifications: chapter 3 pp. 47-56; chapter 6 pp. 122-139; chapter 9 survey : 204-207 |
Saturday afternoon | Data Analysis 1. Search and group items: chapter 5 pp. 106-107; chapter 6 sets pp. 146-153 2. Run lexical, coding and matrix queries: chapter 5 word frequency / text search pp. 110-117; chapter 6 matrix query pp. 141-146; chapter 11 pp. 242-259; 260-265 Data Visualisation 1. Present findings with visualisations: chapter 10 pp. 217-230; 234-241; chapter 2 pp. 28-30 2. Generate reports: chapter 11 pp. 265-269 3. Export content out of NVivo: chapter 5 pp. 119-121; chapter 6 pp. 139-140 4. Coordinate team work: chapter 12 pp. 270-296 |
This is a bring-your-laptop course where you must come with NVivo 10 for Windows installed on your laptop. You can download the 14-day free trial for Windows 10 for Windows here. DO NOT COME TO THE COURSE WITH NVIVO FOR MAC. The NVivo for Mac is incomplete compared to the Windows version (see comparison table here). If you want to run NVivo 10 for Windows on your Mac, consult the compatibility options here. It is your responsibility to ensure that NVivo works well on your machine. No troubleshooting will be provided during the course. Once NVivo is installed on your laptop, you must create a test project to ensure that the software works properly. Follow the instructions below.
As per QSR International
Processor: 2.0 GHz Pentium 4-compatible processor or faster
Memory: 2 GB RAM or more
Hard disk: Approximately 4 GB
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.
Bandara, W. (2006). Using NVivo as a Research Management Tool: a Case Narrative. Quality and Impact of Qualitative Research. 3rd annual QualIT Conference. Document No. 1920952918 Retrieved
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.
Hutchison, A. J., Johnston, L. H., & 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. H. (2006). Software and method: Reflections on teaching and using QSR NVivo in doctoral research. International Journal of Social Research Methodology, 9(5), 379-391.
Leech, N. L., & Onwuegbuzie, A. J. (2011). Beyond Constant Comparison Qualitative Data Analysis: Using NVivo. School Psychology Quarterly, 26(1), 70-84.
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.
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). Retrieved from http://www.qualitative-research.net/index.php/fqs/article/viewArticle/1628/3146.
Wong, L. P. (2008). Data analysis in qualitative research: a brief guide to using NVivo. Malaysian Family Physician 2008, 3(1), 14-20. Retrieved from http://www.e-mfp.org/2008v3n1/pdf/NVivo_in_Qualitative_Research.pdf.
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
Summer School
Research Designs
Summer School
Expert Interviews for Qualitative Data Generation
Issues in Political, Policy and Organizational Ethnography
Qualitative Data Analysis : Concepts and Approaches
Analysing Discourse I, II