I think it’s easy to believe that if you just collect enough quantitative data you can get the answer to any question, and to view that data as being objective. However, there will always be questions that can’t be answered through quantitative means, and data needs to be regarded as dependant upon a context. I talked about this in my pre-seminar text, and nothing we’ve learned during the week has changed my mind.
People who are used to natural science seems to have a real problem grasping issues that can’t be quantified. This is what is called scientism. Since some fields deals with questions that often have fairly easy answers (Did it change, how many did, what was the cause) it’s easy to apply the same logic on other field that doesn’t deal in those easy answers. While a biological study on frogs can have a conclusion of about a paragraph, an anthropological study might have an entire book as the answer, and even that is the short version. That was the main thing I learned during the seminar. In our group we spent a lot of time discussing when to use quantitative and qualitative methods, and it felt like that question dominated the whole seminar.
An example of the different questioned that can be answered was one I posed during the seminar, about tastes in food. If we, in the classroom, want to learn which food is the most popular, that is easily answered by a poll. Simple, quantitative data. However, if we want to know why that food is the most popular, we need to ask about people's opinions, As soon as you get into an area where opinions are interesting you need to use a qualitative method and analysis.
So how about the drum study? Ilias explained that they wanted to know if people moved differently, but you can’t ask that type of question and get a usable answer. The asking of the question to the subject would probably make them change their behaviour, and therefore affect the end result. However using quantitative data that can be gathered without the subject being aware of the data gathered removes the risk of unconscious influence. Here it wouldn’t have worked with qualitative data.
Challenges when working with quantitative research can lie in analyzing your data. It’s important to remember that data isn’t necessarily objective or true in all contexts, and to keep that in mind when you look at it.
You really did a good job describing the difficulties and advantages of quantitative and qualitative method. I especially like your example which helps to distinguish between these two types. Moreover, I guess you are correct in saying that many scientists are sceptical towards qualitative methods. I have to admit that I myself were too critical with these methods. And it is also not given - even if it appears to be so - that quantitative data is objective. A simple questionnaire can easily influence people's answers and therefore the collected data if not designed carefully.
SvaraRaderaFurthermore, I agree with you that it is not easy to understand most of the statistical methods used in papers if you lack the necessary knowledge. But as a scientist it is really important to be able to use statistics as a tool to analyse the data you gathered in research. How else could you try to verify or falsify a theory?
Interesting reflection!
SvaraRaderaI agree with what you say in the last paragraph; the context is important to have in mind since, for example, the time the data was gathered or analysed can have an impact, among many different factors.
Your example from the seminar about quantitative vs qualitative methods and tastes in food was great, but I'm not sure i fully agree with you about the need to use qualitative methods as soon as opinions are of interest. However, I get your point.
Hey!
SvaraRaderaI agree with your reflections on this weeks theme. When talking about scientism, it is interesting how many people suffer from it, even ones who don't practice science.
Your example of research of food popularity depicts usage of quantitative vs qualitative research perfectly. Thank you for your post, very good work!
Hi!
SvaraRaderaYou provide a great discussion on when to use quantitative vs. qualitative methods in research, and thus it seems as if you’ve fully grasped the concepts and their pros/cons. Furthermore, the notion you speak of when you mention quantitative data as being something up for criticism and not necessarily truth in definition, is interesting. I guess it sort of continues the discussion we had in theme 3 where the “truth” in theories were up for debate. However, it seems as if the concept of data is something that would have to be “true” for it to be defined as such, though as you mention it is all based on the means of acquiring and analysis of this data that defines whether or not it is relevant. Great text with concise and thoroughly worked through thoughts on quantitative methods and its uses, keep up the good work!
Good reflection! You cover the essential difference between qualitative vs quantitative which I like!
SvaraRaderaThe last paragraph was a nice touch to the whole reflection! When discussing the data collected, you always have to keep in mind that there are things that influence how and why things are as they are!
Keep it up!
Hi!
SvaraRaderaYou have done a good and well thought out analysis of the theme 5. I agree with you to a great extent regarding objectivity, it is possible to achieve objectivity? According to our previous studies is it clear that it is possible. For example, according to Kant, it is possible to obtain objective knowledge. However, I agree with you is it really possible to achieve objectively?
/Paul