Final Reflection

In the course we’ve been going through many different research methods: quantitative research, research through design, qualitative research and case studies.

Quantitative and qualitative methods are both referring to what kind of data is collected and processed in the research. Roughly, the main difference between the two is that quantitative handles more data which can be processed by various mathematical tools. Qualitative is then the opposite, generally less data but of a qualitative kind, such as written text which requires more manual processing.

These are quite broad definitions so naturally they can be included in both research through design (RtD) and case studies (CS). What I mean is that both of the latter ones can use quantitative and qualitative methods, at least for the analytical part. The methodology for RtD is mostly qualitative since it’s using common design principles. In CS on the other hand, quantitative methods are also used. These methods share a lot of similarities but it’s useful to point out what separates them. An essential difference is in terms of intervention. Given that design is generally an iterative process, intervention is a crucial part of RtD. Through the design you learn which parts work well and which that are problematic. Having the flexibility to continuously make changes to the design is what makes this kind of research good. You can start out with a basic design and then throughout the process make improvements. In this aspect CS is pretty much the opposite. Intervention should try to be kept to a minimum. One could say that RtD is both about observing and interacting, whereas in CS you only have the observational part. It can be argued that some intervention in CS is fine as long as it doesn’t change the phenomenon which is to be observed. It’s important to point out that if too much intervention is done in CS it actually starts to become more and more similar to RtD or a qualitative study.

Both of these approaches are great for new research fields and when the existing literature is limited. These methods have a more explorative nature than just qualitative and quantitative research where the purpose is to answer research questions. For CS and RtD is more about coming up with new research questions, this is especially true for CS. For both it’s about gaining insight into the field. The choices made in the study generally don’t have to be argued for to the same extent as with classic qualitative and quantitative methods. Usually since they’re mainly about exploration, a smaller test population and a limited set of cases are used. A consequence of this is that the studies aren’t generalizable but that’s not really the purpose either.

So this leads to a question of what method to use in which cases? There’s no straightforward answer here because it depends on a lot of factors. It’s good to start by asking what the purpose of the study is. This can actually narrow the options down quite a bit but there are other important things to think about such as: financing, accessibility of tools and test group etc. Sometimes it could actually be good to use a hybrid approach and combining different methods. Having multiple perspectives is generally regarded as something positive and this is something which you get by combining for example quantitative and qualitative methods; just like you can get by having multiple researcher’s conducting a study.

As I’ve pointed out there’s some merit to both quantitative methods and qualitative methods but it seems that some researchers generally prefer one over the other. Personally I like quantitative methods more and since I will write my master thesis this spring I am looking for a project where I’ll mostly use this. However, this is just a preference because my interests lie more within the hard sciences. An advantage with quantitative methods is that they rely more heavily on mathematical tools which is viewed as more objective (which we discussed during the epistemology phase of the course). Obviously it depends, it’s possible to manipulate values to “your advantage”. But at least quantitative methods become less reliant on the researcher’s interpretations.  However, building theory from only quantitative data can be challenging as in a paper I read during the course where the researchers tried to understand the meaning of numerical ratings on Netflix.

In case studies one can use a lot of different tools for for data collection like we saw an example of during the lecture about the “car free year project” where they used diaries, interviews and more. Using multiple tools isn’t only a good idea for case studies but for research in general, especially if the research questions are complicated. Depending on the context, information might be easier to obtain with one tool than another. A potential problem with using multiple tools like this is that the task of analyzing the data becomes more time consuming.

Despite being more time consuming to process, collecting much data provides a good foundation for building theory upon. When building theory you can combine different kinds of data, it doesn’t have to be strictly qualitative or quantitative data. Actually, for strong theory it can be an advantage to do this. For example quantitative data can support a theory which is easier formulated from qualitative data.

In a dream scenario where there are no limiting factors you could potentially use all sorts of different research methods. However, in a realistic scenario there are always tradeoffs you have to make and prioritize the most relevant things for the research. Thankfully quantitative methods have now become more accessible to people compared to historically. Internet and crowd sourcing have made it a lot easier to spread your surveys. Computers have also facilitated the processing of large data sets. For qualitative data this is still a lot trickier to automize even though some very interesting work is being done in artificial intelligence and natural language processing, so who knows what the future of research will look like.