Upgrade from paper with Quirkos

Having been round many market research firms in the last few months, the most striking things is the piles of paper, or at least in the neater offices - shelves of paper! When we talk to small market research firms about their analysis process, many are doing most of their research by printing out data and transcripts, and coding them with coloured highlighters. Some are adamant that this is the way that works best for them, but others are a

Quirkos v1.1 is here!

We are excited to announce that the first update for Quirkos can now be downloaded from here!   Version 1.1 adds two main new features: batch import, and mutli-language reports.   If you have a large number of text sources or transcripts to add to a project, you can now do it all in one go, without having to import each seperately. Just click on the (+) add source button on the bottom right of the source view, and select Import

Spring software update for Quirkos

Even in Edinburgh it’s finally beginning to get warmer, and we are planning the first update for Quirkos. This will be a minor release, but will add several features that users have been requesting. The first of these is a batch import facility, you will be able to import a whole folder of text files, or just multiple files at once. This will be very useful for bringing in all your transcripts in one go, or for importing data from

How to organise notes and memos in Quirkos

  Many people have asked how they can integrate notes or memos into their project in Quirkos. At the moment, there isn’t a dedicated memo feature in the current version of Quirkos (v1.0), but this is planned for a free upgrade later in the year. However, there are actually two ways in which users can integrate notes and memos into their project already using methods that give a great deal of flexibility. The first, and most

The dangers of data mining for text

There is an interesting new article out, which looks at some of the commonly used algorithms in data mining, and finds that they are generally not very accurate, or even reproducible.   Specifically, the study by Lancichinetti et al. (2015) looks at automated topic classification using the commonly used latent Dirichlet allocation algorithm (LDA), a machine learning process which uses a probabilistic approach to categorise and filter large