The case study Dropbox showcases the usage of a Dropbox to organise and share data in a research project.
You can get a short overview of the use of Atlas.ti, a software for qualitative data analysis (QDA). The software was used during a diploma thesis and a post graduate research project in the area of business information systems and organisation.
The following descriptions relate to diploma thesis and a research project likewise. After we conducted and transcribed all the interviews (7 diploma thesis, about 40 in the research project), we qualitatively analysed the transcripts. Finally, we chose Atlas.ti since it was recommended by friends and colleagues. MaxQDA would have been the other option.
First, you create a new project (Hermeneutical Unit). You then add the transcripts to the project. You can further add the audio data to which you can listen later directly during the analysis. Since we transcribed with F4 we could directly import the transcript and the connected audio data with the function Import F4 Document. By using the Family Manageryou can group the transcripts (we group it by company or used questionnaire).
We deductively established some of the codes before the analysis started. We chose especially such codes which represent rather facts than highly interpretative codes (e.g. the job of a person is rather a fact, whereas his opinion towards an incident could be interpreted variably). The biggest part of the codes was created inductively during the analysis. We used the Auto-Coding-Funktion to mark topics which were important for us and which occured quite often (e.g. the term team work was of high importance for us, it was often mentioned. Since we didn’t want to code team work each time manually, we chose to automatically code it.).
After that we read the transcripts and coded manually either by Open Coding or by Code Liste. Subsequently we grouped the codes by using the Code Family Manager.
While reading you hopefully get some ideas for the text analysis, the logical connections made by the interviewees and which hopefully recur in the data. These connections and hypotheses are assessed and connected to the relevant text passage with the Memo option.
At the beginning of the analysis, memos and hypotheses are not completely clear. Further the connections and ideas occur only after reading some of the transcripts. Thus, we recommend to re-read all the data and analyse it twice.
You will use the memos later while compiling the results into the paper or thesis. They can be linked and justified directly with the connected text passages.
– Import F4 transcripts
– While reading the transcripts you can also listen to the audio files
– Available for Windows only
– I used only a few functions, thus, the interface with all its different functions appears to be a bit oversized
|Create project||=>||Create New Hermeneutic Unit|
|Import transcripts||=>||Import F4 Documents / Assign, Associate Documents|
|Read and listen to the transcripts||=>||Open Document / Play – Pause (F4)|
|Coding of relevant text passages||=>||Open Coding, Coding by List, Auto Coding|
|Categorisation of codes||=>||Code Family Manager|
|Create connections/ideas||=>||Create Memos|
|Create hypotheses||=>||Create Memos|
Few days ago a friend of mine asked me about how to learn 10 finger touch typing. I could recall how I learnt it myself and that there was a cool software helping me out. It was free and I had a lot of fun using it. After a short research I stumbled upon it: Tipp10 type tutor. And here we go, it was still alive and is continuously developed. Still open source, it now serves Win, Mac and Linux users and even an online version exists.
I can really recommend Tipp10. It is clearly arranged and doesn’t need any further explanations. You can type training lessons, free lessons (even a C++ example class for future programmers) or your own created lessons. There are great statistics about your learning improvements and the error types you make sorted after the concrete finger or sign/key. To learn the 10 finger touch typing was more like playing a video game than learning.
Though, the software is not usable in the social science research process and therefore a bit off-topic. Still, in our view it is always beneficial to be able to write fast on a keyboard. Your brain loves thinking and dislikes wasting time on searching the keys…. ;)