- Import keywords into atlasti manual#
- Import keywords into atlasti software#
- Import keywords into atlasti code#
It lets you add metadata to documents which it calls variables (I have e.g.
Import keywords into atlasti code#
It also has some powerful visual tools of its own (comparing code frequencies, code relations, word clouds, code maps, document comparison charts, etc., see link below). MAXQDA also does various reports on tags (codes) relationships which you can export to a spreadsheet and then, for example, import and visualise in Tableau (which I also did). It is also easy to narrow down searches and analyses as you can only work on parts of a dataset (in MAXQDA terminology, you can ‘activate’ only a subset of documents matching a particular query (documents matching specific criteria, including codes/tags etc.) and then you work further only on that subset). If you need to identify themes that often show up together, to look for cases where a number of tags are mentioned and some others are not, or to actually look for cases where tags are mentioned within some defined distance (proximity), this is an extremely powerful tool for content analysis (or critical discourse analysis, as mentioned earlier in this thread already). MAXQDA is also great at doing various queries to discover correlations between tags, and there are several helpful ways to visualise relationships between tags (codes), for example, to see how the codes relate to each other and how “close” they are with regard to how often they appear together.
MAXQDA is then great in identifying overlapping tags (themes), which was a critical aspect for me as I needed to find out who said what, when, and to whom publicly, so I used MAXQDA to look for overlapping tags representing themes and tags representing one or more actors. This is what MAXQDA looks like, with tags (codes) displayed in a document: If you are certain of exact matches in documents, it could be helpful as a feature.
Import keywords into atlasti manual#
I ended up with some 330 codes/tags (to identify themes, patterns, and actors) and have assigned them in total almost 9000 times across the dataset.Ĭoding/tagging is manual work there is autocoding in MAXQDA which may help a bit though if you know you are looking for exact keyword matches (see my post above) but as I was looking for themes across a large dataset, not necessarily using the same keywords every single time (this was media coverage after all so people use different words to express the same ideas or thoughts), I did not use it much. I could predict some codes and themes in advance, but the majority of them have surfaced while actually coding/tagging). I have developed an extensive coding system (both inductive and deductive coding were used, i.e.
Import keywords into atlasti software#
The challenge was that I was faced with many documents to read and go through.Īs mentioned already in this thread, QDA software is great in that you tag (code) themes, patterns, etc. The idea was to find the most salient themes with regard to social, economic and political aspects and other issues surrounding the project and to identify the most prominent actors who were actively promoting or discussing them (it was a case study in communications and public relations). You are probably better off with DEVONthink but perhaps this may someday be helpful to someone else as well so here’s my take on it.Ī bit of a background as I feel understanding the use case is important: I did a content analysis (CA) on about 1300 pieces of media coverage of a particular project, spanning almost 20 years. I’ve been meaning to post this sooner but had been quite busy these days. I am not sure whether this is entirely relevant to your question, but I used MAXQDA and DEVONthink while working on my master’s dissertation last year, so I have some experience with both however, I have used them for different purposes.
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