I think that you bring up something interesting regarding journals’ different preferences for one type of research methodology. There are obviously pros and cons with qualitative and quantitative methods and saying that one is generally better than the other is a problematic position to take. They’re useful for different purposes and can yield different types of results. There can of course be limiting factors that will determine which methods can be used. I guess quantitative methods are regarded as more objective since they rely on mathematical tools and therefore they are more preferable. However, for answering why-questions they might not suffice. So a question to think about could be why should you prefer on over the other? One could also combine these methods to broaden the perspective.
I agree with you that case studies are quite flexible. I do also see a strong connection between case studies and research through design. In both, the researcher seems to have a lot of freedom to shape the research and choose which methods to use. These choices don’t need to be explained to the same extent as compared within a purely qualitative/quantitative study. Often it seems like they do resort to using well established tools. Also as you point out
it’s about exploring a new field. When doing so it seems common to use multiple tools of data collection to reduce the risk of having too little useful data. Introducing new research tools won’t have as severe consequences if there’s enough provided by other tools.
I was also a bit confused about case studies before the lecture and the seminar. I still thought it was quite flexible when it came to choosing research tools and methodologies but I thought it was very strict that the researcher wasn’t supposed to intervene. However, like we discussed during the seminar, I do agree that as long as the phenomenon isn’t in itself altered, the interventions are okay. But it’s a fine line to cross. In the research on being car free the researchers were balancing on this line. One could argue that they got extra motivation from some of the documentation tools they were provided. That’s maybe not the best example but instead of just observing (which I thought was the rule for case studies) the researchers actually affect the test group. But as long as the influence isn’t too great the study can probably provide valuable insights into the field.
I feel the same way about case studies. The fact that they try to capture real life behaviour and not behaviour in an artificial environment makes it very interesting. This is also why I’m skeptical and critical to researchers intervening in them. The researchers have to make sure that their interventions are not altering the observed scenario too much. Case stuides are very much about exploring, but if you explore something which is made up it’s turning into an organized experiment.
I think it’s interesting, what you point out, that the sample was selected cautiously. I think the fact that they picked highly motivated participants was because it would be hard to find people to voluntarily sign up for a study like this if they weren’t. But this also has implications on the study. The hypothesis that “people will save money by not using a car” will be easier to confirm with a highly motivated test group. A less motivated group might end up using more expensive alternative. So the sample is obviously important for the generalizability of the study. However, generealizing is often not the goal but rather to come up with research questions, as we’ve discussed in class. Still I do think that with a too narrow sample you run a risk of coming up with research questions which are maybe not relevant for a larger population.
What I’ve always found problematic with semi-structured interviews is that it can be quite tough to find patterns between the participants’ answers. Another thing is that you also might come up with good follow-up questions during one of the last interviews, which you wished you had asked during all the interviews. I guess it could be a good thing to do pilot studies in order to analyse what potential follow-up questions might arise. I do agree that it can be good if the participants have the right knowledge, but I do also think that differences in knowledge can yield interesting results. It really depends on what the purpose of the study is. Like with your example about couples, it’s obviously a requirement that they have a certain knowledge. Maybe this is more often a requirement for more specific research questions.
A agree that you can compare witg existing literature and research in order to try to generalize. However, I think as we spoke about in class that a case study is something that you do when the field is new and the existing research on the area is limited. So it could be quite hard to only try to increase the generalizability this way. Better ways I think would be to study more cases and use a larger test group and cross compare these. The main goals with case studies is to gain insight into the field and come up with research questions so generalizability isn’t necessarily a high priority.
You bring up that case studies start with a broad research question and then tries to narrow it down. I do agree in a way, the number of questions doesn’t have to decrease (most likely they should increase) but they should be more specific.
I was also a bit confused that case studies didn’t need a hypothesis when starting out. I was also taught that you need that before. I guess it makes it a bit broader since a hypothesis is usually quite narrow. Research questions gives you freedom to explore a lot more because hypotheses quite narrowed down and confirmed or denied. What I like about case studies is that you don’t set out expecting to find something. It can also make it hard to know what you should look for. That’s why it can be useful to use many different tools for observing and acquiring data. This could also make the data analysis step quite complex. Looking for patterns between cases is one thing you could do but that would require you to look at multiple cases.
Interesting that you bring up population selection. As you say all people are different, so in order to conduct an optimal study you would have to have all people in the world which of course isn’t feasible. But you could also target different groups e.g. a certain age range which would narrow down the population. Still it’s very interesting to read the motivation of how the population was sampled. In my experience, accessibility is a dominant factor but it’s also closely linked to otherfactors such as financing. I don’t know if there exists a standardized way of sampling a population ( there probably is in some sense) because it depends so much on the type of research being done and where it is performed. It’s probably easier to motivate if it’s a certain skill or knowldge required from the participant.
I also find the financial aspect of research quite interesting. I as well have thought about the fact that qualitative research is more expensive, especially for larger studies. This is a real benefit for quantitative methods and important factor to take into account when designing a study. I wonder if a lot of originally qualitative research ideas are turned into quantitative research because of this.
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