Post reflection - Theme 4: Quantitative methods

This week’s theme was “quantitative research methods”. That means methods that depends on numerical measurements and where the resulting data is numerical, for example surveys. An important aspect of quantitative studies is that the data needs to be analysed using statistics to be able to say something on a general basis. You need to use statistics and logical arguments to prove a theory.

During the seminar we talked a lot about quantitative studies vs qualitative studies, and their limitations and their benefits. One easy way to distinguishing the two is that quantitative studies have their roots in natural science and qualitative studies have their roots in social science. Benefits with quantitative methods is that are easy to do on a larger scale and easy to analyse because of the data collected only consists of numbers. In quantitative studies you get other kinds of data that maybe easier to answer to questions like “why” and find out effects and motivation behind actions. But when it comes to benefits and limitations it isn’t as black and white as I thought and we discussed how you can get an answer on a “why” question even with quantitative methods.

We talked about how the experiment in this week’s paper was a good example of this. What they did in their study was that they before they started had an idea of what kind of analysis they would do and what theory they wanted to prove. They kept this in mind while they designed their study. Designing the study is one of the more important parts, since this will decide what kind of answers you can and will get. At the end they used logical arguments in a good way when analysing the data to prove their theory. But I think that in some way this study has some common things with qualitative studies since it was kind of profound way of conducting the study, which took time and effort, and that would require a lot of resources to implement on a larger scale. Instead of having people observe the people playing the drum they let the computer to do the same. Which gave them data to back their theory and that feels more secure and impartial since it don’t rely on human perception and analysation.

We also talked about bias in research and how you never can reach a non bias study but that you should strive for it. For example when you do a quantitative study you have to think carefully about what questions you use and how you formulate them. In a qualitative study you have to be careful in how you interpret your ‘data’ and analyse it to get it as objective as possible.