Course Overview
Course Description
NVivo is a digital research tool designed by QSR International to assist qualitative and mixed-methods researchers. If you are working with unstructured data such as interview transcripts, speeches, videos, or even twitter data, then NVivo can help save time during your research.
In this course, you will learn how NVivo can assist you with your research projects. You will understand how NVivo is embedded within your broader research methodology. This course is free to University of Melbourne researchers.
You can participate in this course as often as you like until you have assessed your own learning as successful.
As part of our e-learning content creation over the Summer, I put together this professional, serious, and informative infomercial for NVivo 12 at the University of Melbourne. Watch at your own peril!
Training Format
This course is separated into individual modules. Whilst the modules are designed to be accessed during an online learning workshop (via Zoom), they are also accessible for those who cannot attend.
Each module consists of four sections with an associated challenge and learning objective attached. Whilst the online training sessions will run for around 40 minutes with 20 minutes for unstructured questions, students are encouraged to work through the sections at their own pace.
The modules are as follows:
These are difficult times, and we are not primarily an online teaching service. When we are running are face to face workshops, things run a little bit differently. Instead of six 40 minute modules, I instead run a 2 hour introductory workshop, a 2 hour intermediate workshop, and a variety of advanced meetups where members of the NVivo community at the University of Melbourne take the lead.
Learning Objectives
Students who have completed this course will understand how to:
Manage a research database in NVivo
Create and iterate on a coding framework in NVivo
Generate a range of visualisations underpinned by data analysis
Use NVivo for specialised research methodologies for literature reviews and web scraping
Intended Learning Outcomes
Students who have completed this course should have acquired:
Computational Skills: The ability to adapt existing computational methods and tools to complete a target task
Problem Solving Skills: The ability to engage with unfamiliar problems and identify relevant solution strategies
Analytical Skills: The ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis
Critical Thinking Skills: The ability to critically evaluate both qualitative and quantitative data
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