ECPR

Install the app

Install this application on your home screen for quick and easy access when you’re on the go.

Just tap Share then “Add to Home Screen”

ECPR

Install the app

Install this application on your home screen for quick and easy access when you’re on the go.

Just tap Share then “Add to Home Screen”

Introduction to NVivo for Qualitative Data Analysis

Marie-Hélène Paré
info@mariehelenepare.com

Universitat Oberta de Catalunya

Marie-Hélène teaches qualitative research methods at the Open University of Catalonia (UOC) and is a freelance methodologist in qualitative data analysis. She was educated in Quebec, Beirut and Oxford where she read social work. A clinician by training, she worked as a mental health officer in humanitarian missions for MSF, MDM and UNWRA in psychosocial aid programs for survivors of war trauma in East Africa and the Middle East. Her clinical work led her to research the harm that INGOs can do in the name of doing good when imposing Western paradigms in culturally and politically different contexts.

She is an NVivo Certified Platinum Trainer and is part of the NVivo Academy training team for the NVivo online courses. Marie-Hélène is a sought-after methodologist who has taught qualitative data analysis in more than sixty universities and research centres worldwide, in countries including Qatar and Iran. Since 2009, she has taught the introductory and advanced courses in qualitative data analysis at the ECPR Methods School and teaches similar courses at the IPSA-NUS Summer School and for the Methods Excellence Network. Her methodological interests range from advances in qualitative data analysis,  qualitative evidence synthesis, postcolonial epistemology and participatory methodologies. Read more about Marie-Hélène.

 @TheQualAnalyst

Course Dates and Times

Friday 2 March
13:00–15:00 and 15:30–17:00

Saturday 3 March
09:00–10:30 / 11:00–12:00 and 13:00–14:30
or
11:00–12:00 / 13:00–14:30 and 15:00–16:30

Prerequisite Knowledge

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

THIS COURSE USES NVIVO 11 PRO FOR WINDOWS

This is a bring-your-laptop course for NVivo 11 Pro for Windows. Download the 14-day free trial

This course is unsuitable for NVivo 11 for Mac because the Mac version is incomplete. You can run NVivo 11 Pro for Windows on a Mac using Apple Boot Camp or Parallels if, and only if, your Mac meets these system requirements.

You must ensure that NVivo works well on your machine regardless of the OS, because no technical assistance is provided at the Winter School. Find more on installation instructions in the sections on Software and Hardware, below.


Short Outline

On this course, you'll learn how to use NVivo for the management, coding, analysis and visualisation of qualitative data.

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.

The course is entirely hands-on and uses sample data to teach NVivo’s basic and advanced functionalities.

I will not cover how to analyse qualitative data in NVivo using thematic analysis, grounded theory, or content analysis. To learn how to do this, take the course Advanced Qualitative Data Analysis.

Full course outline, including diagrams and demos


Long Course Outline

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.

Day Topic Details
Friday Data organisation and exploration
  1. Introduction to NVivo: interface and menus
  2. Import and organise data in a project
  3. Manage a literature review
  4. Explore textual data
  5. Links to external information
  6. Memoing’s one research
Saturday morning Data coding and comparison
  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 Data analysis and visualisation
  1. Search tools
  2. Prepare the analysis with sets
  3. Run coding and matrix queries
  4. Present findings with visualisations
  5. Generate summary reports
  6. Export data and findings
Day Readings
Note

The NVivo 11 Pro Started Guide is the main text of the course.

For a better understanding of how to use NVivo in qualitative research, read Bazeley & Jackson (2013) Qualitative Data Analysis with NVivo, 2nd edition

NB: this book was written for NVivo 10. Some functionalities and dialog boxes are now outdated.

Friday

Data organisation and exploration

Compulsory text

The NVivo 11 Pro Started Guide pp.5–7; 10–14; 17–23; 37–38

Optional text

Bazeley & Jackson: Qualitative Data Analysis with NVivo, 2nd edition format data: 5961; download data with NCapture: 173177; import data: (internals) 2434; 4546; 6166; (open-ended surveys) 199203; (social media) 171176; 209211; (multimedia) 154167; transcription: 167169; externals: 6263; literature review: 178194; links and memos: 3445; text-based queries: 110117; 249250

Saturday morning

Data coding and comparison

Compulsory text

The NVivo 11 Pro Started Guide pp.24–36

Optional text

Bazeley & Jackson: Qualitative Data Analysis with NVivo, 2nd edition autocoding: 108110; (datasets) 207208; codes and coding: 6894; coding scheme: 95106; 117119; relationship nodes: 230–234; cases and variables: 5056; (from surveys) 122139; 205207

Saturday afternoon

Data analysis and visualisation

Compulsory text

The NVivo 11 Pro Started Guide pp.40–48; 15–16

Optional text

Bazeley & Jackson: Qualitative Data Analysis with NVivo, 2nd edition sets: 106–107; 146–153; coding-based queries: 141–146; 242–248; 250–257; cross-case analysis and theory-building: 257–265; visualisations: (model) 28–30; 217–230; 234–241; reports: 265–269; export content out of NVivo: 119–121; 139–140; team work: 270–296

Software Requirements

This course requires you to run NVivo 11 Pro for Windows on your laptop or, alternatively, NVivo 11 Plus.
Download the 14-day free trial

This course is unsuitable for NVivo 11 for Mac because the Mac version is incomplete. You can run NVivo 11 Pro for Windows on a Mac using Apple Boot Camp or Parallels if, and only if, your Mac meets these system requirements.

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

Once NVivo is installed, verify that it works properly by following these instructions:

  1. On your Desktop, launch NVivo by clicking on the NVivo 11 shortcut icon.
  2. On the Start screen, in the New section, click Sample Project.
  3. NVivo opens a copy of the sample project which is stored in your default project location.
  4. If you can’t open the Sample project, submit a support request form online (see section Contact Us Online at the bottom).

NVivo system requirements

 

Minimum

Recommended

Processor

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

2.0 GHz dual-core processor or faster

Memory

2 GB RAM or more

4 GB RAM or more

Display

1024 x 768 screen resolution

1680 x 1050 screen resolution or higher

Operating system

Microsoft Windows 7

Microsoft Windows 7 or later

Hard disk

Approximately 5 GB of available hard-disk space (additional hard-disk space may be required for NVivo project data)

Approximately 8 GB of available hard-disk space (additional hard-disk space may be required for NVivo project data)

 

 

Hardware Requirements

See software requirements.

Literature

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

<p><strong>Summer School </strong><br /> Research Designs</p>

Recommended Courses to Cover After this One

<p><strong>Summer School</strong><br /> Expert Interviews for Qualitative Data Generation<br /> Qualitative Data Analysis: Concepts and Procedures<br /> &nbsp;</p>


Additional Information

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.