Quirkos v1.4.1 is now available for Linux

  A little later than our Windows and Mac version, we are happy to announce that we have just released Quirkos 1.4.1 for Linux. There are some major changes to the way we release and package our Linux version, so we want to provide some technical details of these, and installation instructions. Previously our releases had a binary-based and distro independent installer. However, this was based on 32 bit libraries to provide backwards

Quirkos update v1.4.1 is here!

Since Quirkos version 1.4 came out last year, we have been gathering feedback from dozens of users who have given us suggestions, or reported problems and bugs. This month we are releasing a small update for Quirkos, which will improve more than a dozen aspects of the software:   MacOS – Since our last version, a new version of Mac OS X (now called macOS) has been released. This actually caused a few minor glitches in Quirkos, we

What next? Making the leap from coding to analysis

  So you spend weeks or months coding all your qualitative data. Maybe you even did it multiple times, using different frameworks and research paradigms. You've followed our introduction guides and everything is neatly (or fairly neatly) organised and inter-related, and you can generate huge reports of all your coding work. Good job! But what happens now?   It's a question asked by lot of qualitative researchers: after all this

Comparing qualitative software with spreadsheet and word processor software

An article was recently posted on the excellent Digital Tools for Qualitative Research blog on how you can use standard spreadsheet software like Excel to do qualitative analysis. There are many other articles describing this kind of approach, for example Susan Eliot or Meyer and Avery (2008). However, it’s also possible to use word processing software as well, see for example this presentation from Jean Scandlyn on the pros and cons of

Making the most of bad qualitative data

  A cardinal rule of most research projects is things don’t always go to plan. Qualitative data collection is no difference, and the variability in approaches and respondents means that there is always the potential for things to go awry. However, the typical small sample sizes can make even one or two frustrating responses difficult to stomach, since they can represent such a high proportion of the whole data set. Sometimes