In my case I'm putting in here the information about the songs: artist, country, score, placing, etc.: Case classifications are the demographics of your case: maybe you interviewed some great operatic diva from the stage, some jazz heroes from the local club, and some rock'n'roll kids from satellite TV: here's where you'd put all the useful background information about them. File classifications may be things about the file itself: what type of file it is, when it was recorded, etc. You might've interviewed them twice so there'd be two files associated with them (or maybe even more), but they're the one case.įiles and cases also have associated "classifications".
Each person might be considered a "case". Let's say you've done several interviews with different people. It's important to stress that NVivo's a pretty open environment and you can use these fields how you like, but there are some standard principles.
But what are these codes and cases? Cases and classifications The first of these is relatively straightforward: we've just imported a load of files. And down the left-hand side of NVivo's navigation pane are three important subsections: Files, Codes, and Cases. Helpfully it's been built to look like a Microsoft Office application, so that makes it a bit easier than it could be to navigate. You're going to have to do a lot of the hard work.īut that's no reason to go running scared from NVivo. It's more like a glorified highlighter pen that can add up. It's not an artificial intelligence tool. Now what?įrustratingly, it's not just as simple as saying "Hey, NVivo, my love: shine a light on these texts. I've sourced the lyrics to all the 1974 Eurovision Song Contest entries (translated into English where necessary) and I've imported them into NVivo.
all kinds of other materials you might want to analyse like emails, tweets, transcripts, video. You can import all kinds of everything into NVivo: the spreadsheets you've collated, the bibliographic data you've amassed, the voice recordings you made when you were conducting interviews. The first thing NVivo needs is some data. NVivo is a tool to facilitate that analysis. And that sort of thing is a bit harder to automatically analyse in a meaningful way. But a lot of data we get is in the form of text of words. You can throw in a load of numerical data and get really quite sophisticated analysis at the touch of a button. And spreadsheets are really good at that. Most data analysis is quantitative: it's about counting numbers. Her publications include articles in International Journal of Urban & Regional Research, City & Community, Global Networks, and Ethnography.NVivo is a qualitative data analysis tool. For over two decades, she has been engaged in qualitative research and teaching.
#DEMO NVIVO 12 HOW TO#
The workshop will include information about Stanford resources for learning how to use these tools.Īlesia Montgomery is the Subject Specialist for Sociology, Psychology, and Qualitative Data at Stanford. Programming language: Python (free to all)Ĭome find out the basics about (1) how to choose the right tool for you (e.g., based on your epistemological assumptions, research questions, dataset size/type, data management needs), (2) how to get these tools, and (3) how to use these tools to qualitatively “code” your data.Open source software: RQDA (free to all-an R package).Commercial software: NVivo (free to Stanford faculty, students, staff).Qualitative Research Tools: NVivo, RQDA, Python with Alesia MontgomeryĪre you doing a qualitative analysis of “unstructured data” (e.g., interview transcripts, government documents, observational videos)? This demo will show three tools: