I’m an anthropologist trying to use scrivener as both a word processor and a qualitative data analysis software. I’m quite new to Scrivener but I can see it has an incredible potential to substitute QDA such as Nvivo and Atlat.ti. I’'ve only found two existing topics on this matter and they are quite old so I’d like to open a new discussion.
https://forum.literatureandlatte.com/t/keyword-tagging-of-text-passages/14151/14
https://forum.literatureandlatte.com/t/ethnography-data-to-manuscript/12259/8
I’m still trying to figure out my own coding style and the overall workflow to analyze fieldnotes and transcriptions of interviews. However, It seems that the complex system of comments, keywords, documents links etc. that Scrivener provides could work very well for this porpuse. I’d like to suggest how to code material, within scrivener, by using a system similar to Nvivo’s “nodes” . I’d also like to suggest a new feature that allows the direct coding/marking of files audio/video (my wish list!). And lastly, I’d like to ask some advice for working with co-occurrencies in the coding system I’ve designed for my research. Any suggestion is more than welcome!.
Analytical nodes in scrivener. In Scrivener you can easily create “nodes” like in Nvivo. You only need to create a folder that works as “container” of quotations, exerpts or sections that you want to link from other documents. Let’s say you are trying to analyze the opinion about housing prices that emerges in your transcriptions. You first create a document/node called “opinion about housing prices”. After that, any time you find a section in which informants give their opinion, you select it and link it to the document/node you have created. In this way you can easily retrive all the sections in which informants have discussed the topic by browsing the document/node’s inspector panel. In particular the “document references” panel will show all those links you have created, thus allowing you to open the sections in windows, re-read them, make comparisons etc.
I find this method a bit better than working with keywords, because it gives the user more flexibility. Keywords can be added to folders or documents that have previuosly been created in the binder. So there no way to select excerpts or passages arbitrarily, which is exaclty what you need to do when you are re-reading your material for coding. Also the use of comments can be confusing, since from the perspective of the researcher, comment and coding are two different mental processes at two different analytical levels. By creating nodes like this, and by coding through links, you don’t need to change the structure of your main document, nor you need to mix comments with codes.
It is also very flexible in terms of reorganizing the nodes with the progression of the analysis. When you find new connections and possibly create new nodes, you can easily move existing nodes in new folders and sub-folders. Moreover, nodes are also texts files in which you can start writing memos that refers to that connection.
Coding and marking files audio/video. However, I see at least two limitations in this system. As far as I know, Scrivener has limited features for working with audio/video. It doesn’t allow the selection of portions of files audio/video for adding comment or links, and it also doesn’t have an audio/video marking system (Am I right?). As a consequence, since you can’t link portions of files audio/video to other documents, my coding workaround, works only with texts. If you want to select the section of the audio/video interview in which informants (again) talk about “housing price”, you can’t do it. YOu have to transcribe it first and then select the text to be linked.
I would suggest to add two simple functions, which will benefit all sorts of academics/journalists/writers working with audio and video material. 1) As I just said, some feature that allows the selection of portion of audio/video files. It would be nice to add comments or annotations that refer to that specific section instead of the whole file (like you do with a text file). It would be also very useful to link those sections to other documents, like the “nodes”.
2) I would suggest to add some kind of audio/video marking system thought which you can link your transcriptions to the audio/video file themselves. In this way, when you are re-reading the transcription, you could open the audio file directly in the section you want, without having to find it manually (this feature is also present in QDA software).
Co-occurencies. Another limitations regards co-occurencies. The method I have designed allows the user to link the same excerpt to different nodes. If for example you find a section of the transcription in which the interviewee link “housing prices” to “gentrification”, you can code the exerpt with both codes (just by linking it to the nodes you have created). If you were using comments, for example, this wouldn’t be possible, since you cannot add two different comments to the same exerpt. However there is no way to rethrive and group toghether co-occurrrencies like this one. By using this method you will create two different document/nodes with their own document references (“housing prices” and “gentrification”), but you cannot detect the documents references (or link) they share. Let’s say you want to recall all the sections in which informants link “housing prices” to “gentrification”, you have to do it manually by browsing the document references of the two nodes. It would be great to have some functions that help to do this. Something that could compare the references of two or more documents (in my case document/codes) and detect the common links. Any suggestions to solve this? maybe there is also some other way you can achieve the same result.
I hope I made myself clear, it is not an easy task for a beginner like myself.
I love Scrivener and I think it is a great software at an affordable price. As an academic working with qualitative data, I’d love it even more if it could integrate these functions. Users like me would be able to carry out all the research process (data gathering, analysis and writing) within the same software. Just amazing!