Quantifying qualitative data can be tempting. When we code or tag our data in Qualitative Data Analysis the computer keeps counting. With little effort we can produce overwhelming tables and great visualisations. However, we need to think of quantification before we start coding and make proper decisions about our analytical units in order not to spoil good qualitative research with inappropriate quantifications. A good strategy here is to make the coded segment reflect text immanent structures or to get advice on our analysis plan before we invest many days into coding.
Continue reading Coding With the End in Mind: Quantifying Data Done Right →
Software is a tool that helps us to accelerate, improve or even enable our research. However, as researchers trying to answer questions that are relevant in our fields, technology must not drive our research but rather be shaped by it. For this, it is important that we never use software for software’s sake and rather are aware how subtle yet strongly the digital transformation changes the way we work and think.
Continue reading Software aware or software driven? When (not) to use QDA software →
First and foremost Humanists describe what is and only in a second step answer why it is that way. Qualitative Data Analysis and many similar research techniques often follow a simple scheme: creating categories/variables/topics, applying them to text and then analysing meaningful groups by qualitative and quantitative comparison. By this, we easily fall into the “Positivist Pitfall” of only describing, instead of explaining our data. However, we must strive to create meaningful interpretations that can be disputed or even be proven wrong.
Continue reading The Positivist Pitfall →
On the surface MAXQDA and ATLAS.ti seem almost identical. Yet when we look under the hood, we see strong differences: one follows the logic of a relational data base and sorts everything into neat categories and the other operates like a graph data base that links different entities to form a large network. The implicit potentials and constrains of each (and any) software commonly drive our research because we too often follow the road that we already know best.
Continue reading MAXQDA or ATLAS.ti? How software shapes research →
If we want to quantify the results of our coding in QDA, we have to think about two aspects first: scope and quantity. The scope forces us to consider what length our text segments should have (words, sentence, paragraph), whereas the quantity indicates what we are allowed to do with these segments later in analysis (numerical, boolean, non-numerical analysis). To quantify codes properly, this must be taken into consideration long before we complete our coding. Continue reading Scope & Quantity: Defining Codes for Proper Quantification →
No, it’s not. Granted, QDA-software is far from methodologically neutral and strongly influences the way we design our research. However, it is not a method in itself and we always need some external “building plan”(a method) to guide us. Continue reading Is (MAX)QDA actually a method? →