In a nutshell Qualitative Data Analysis is all about structuring unstructured data, be it text, images, audio or video, by manually assigning tags, categories or “codes” to segments of data. This helps finding relevant segments again and allows to quantify (count) them.
As the name suggests, Qualitative Data Analysis is about analysing qualitative data. Therefore, if you are exclusively interested in numbers and tables, you might have come to the wrong place. With qualitative data we generally mean unstructured text, but it also includes images, audio or video files. Basically qualitative data encompasses the entire “Qualitative Diversity” (Rädiker, Kuckartz 2019, p. 3) out there. If we want to sound more traditional, we could simply call them “sources” or “documents”, although in this case we might lose some hipness-points with all the data nerds out there. In fact, when we read a newspaper, look at a historical painting, watch a Youtube video or listen to a podcast we always do some kind of qualitative data analysis.
In any case, Qualitative Data Analysis is not just concerned with qualitative data but also with qualitative analysis. Here, qualitative is not a synonym for “good”, it rather implies a stronger interest in the rare and specific; less in the plenty and general. Qualitative methods are generally dominated by reading, interpreting, taking notes and manually leaving “bread crumbs” to find all the way back. Therefore, it has less to do with maths, although it requires quite some statistical literacy. For example, when we start counting or visualising the segments we have coded we must be very careful not to create invalid results!
Still, qualitative data does not necessarily need to be analysed in a (purely) qualitative way (Kuckartz 2014, p. 15). There are many ways to analyse qualitative data with quantitative methods, ranging from simple word frequency analysis, over text mining to sophisticated topic modelling approaches. Although some QDA-software also makes use of such techniques and combines qualitative and quantitative approaches (which is called: Mixed Methods), at the heart of QDA remains the manual reading, interpreting and grouping of text segments.
Qualitative Data Analysis is not a method. Instead, QDA can be used with many methods, such as Content Analysis, Grounded Theory, Discourse Analysis or Narrative Analysis. What unites QDA (for good or worse!) is often the use of similar software. Some researchers call them CAQDAS (Computer Assisted Qualitative Data Analysis Software) such as Susanne Friese (2016, p. 9-10). Others, like Kuckartz (2014, p. 173), prefer to call them QDAS instead (Qualitative Data Analysis Software). Here, we will follow the latter, because the acronym is shorter, and when you think of it “Computer Assisted … Software” is quite a strange name.
The three major (and unfortunately commercial) software programs out there are ATLAS.ti, MAXQDA and NVivo. However, they all provide the same basic functionality: They help us in assigning tags/categories/codes to segments of data and quantify, analyse and visualise the results. They each have their individual strengths and weaknesses (we will go into that another time) and they are all around since the 1990s, so they probably have come to stay.
How do we carry out QDA?
That is a very good question, and it would deserve a very long answer! But if we must have it quick-and-dirty: We read text, assign codes to it (called “coding”), take notes and analyse the results. Then we create a report, based on the coding we did and the observations we made.
Of course much more can and will be said about QDA and how (not) to do it. We will dive more deeply into this topic in later articles. For a start, we can take a closer look at:
- Creating a good, persistent and consistent code system
- Whether QDA is a method or a tool
- The different analytical units
- A comparison of the different QDA-software
- Mixed Methods approaches breaching the qualitative/quantitative divide
- And, my personal friend, the spooky and dangerous: Positivist Pitfall!