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We move on to inductive coding and learn tools to code data manually. 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 start by autocoding questions from structured interviews, so the responses of each question are gathered in one node. I introduce techniques to autocode and code data inductively in NVivo. We analyse the outputs using word clouds, dendograms, and wordtrees. Module 1 concludes with lexical queries which search for frequency, occurrence, and context of keywords in textual data. We create externals that link a NVivo project to outside information, and memos where the analytic process is recorded. I explain how you can work directly on pictures or generate a log to associate comments with specific picture regions.

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I will explain how to work with still images.

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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. 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 then move in NVivo and import and organise a range of qualitative data. We review how data can be organised in comparative and non-comparative designs, coding approaches developed, and types of analyses conducted. I open with notions of qualitative research designs and their application in a NVivo project. You will gain the knowledge and skills to use the basic and advanced features of NVivo in your own research. NVivo supports a range of inductive and deductive methods to qualitative analysis such as 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 is a software programme for qualitative data analysis. To learn how to do this, take the course Advanced Qualitative Data Analysis.įull course outline, including diagrams and demos I will not cover how to analyse qualitative data in NVivo using thematic analysis, grounded theory, or content analysis.

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

present qualitative findings using graphic displays.

The content is spread over four modules and will teach you how to: On this course, you'll learn how to use NVivo for the management, coding, analysis and visualisation of qualitative data. Find more on installation instructions in the sections on Software and Hardware, below. You must ensure that NVivo works well on your machine regardless of the OS, because no technical assistance is provided at the Winter School.

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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.

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This course is unsuitable for NVivo 11 for Mac because the Mac version is incomplete. This is a bring-your-laptop course for NVivo 11 Pro for Windows. Her methodological interests range from advances in qualitative data analysis to qualitative systematic reviews, postcolonial epistemology and participatory methodologies. Since 2009, she has taught the introductory and advanced courses in qualitative data analysis at this Methods School, and she teaches similar courses the IPSA-NUS Summer School in Singapore. Marie-Hélène is a sought-after methodologist, having taught qualitative data analysis in more than fifty universities and research centres worldwide, including universities in Qatar and Iran. She is an NVivo Certified Platinum Trainer and is part of the NVivo Academy training team for the NVivo online courses. 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. 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. She was educated in Quebec, Beirut and Oxford where she read social work.

Marie-Hélène Paré teaches qualitative research methods at the Open University of Catalonia (UOC) and is a freelance methodologist in qualitative data analysis.

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