Arozin - Last reflection

This course have been one big roller coaster for me. I couldn't get a grip on its application and purpose from the start, but during the course I slowly came to appreciate it. The first part of the course was about theory and the definition of knowledge and both of them are deep subjects that requires a somewhat creative mind and being able to think outside the box to comprehend. I did find both of the subjects quite fascinating but I had a hard time understanding why they were relevant. Somewhere in the middle of the course we introduced the concept of hypothesis and the definition and usage of theory started to show itself. The connection between them and their purpose started to show itself. It was during a seminar when we discussed the differentiation between theory and hypothesis when we came up with a pretty good definition of their connection and their similarities and differences.

We defined that empirical data is the link between theory and hypothesis, where theory are often based on assumptions and the interpretation of similar cases, the study of empirical data, and the application of it to build your theory transforms it into a hypothesis.

I especially came to enjoy the course in the end, where we combined the other themes and started to make sense of it all. Thereby their correlation became somewhat clear.

During the last year I have found a fascination of data analysis of quantitative data, and how to extract and use their hidden information for your gain. The combination of statistical analysis and quantitative methods combined can be really effective while trying to make sense of big data. So during this course the part that treated the application of quantitative methods and their application cought my attention.

I took another course during this semester, industrial marketing, where one part of the course treated the use of big data and the purpose of applying quantitative methods and evaluations to create marketing concepts and campaigns by analyzing it. This is a method that the worlds biggest IT companies are working with today (i.e. Google, Apple Microsoft), but since big data usually means terabytes or even petabytes of raw data, its hard to apply a “regular” method to extract something of it.

The advantages of quantitative methods are vast, but one that I think is especially mentionable is that you get results that are easy to compare. Since quantitative methods usually comes down to surveys and such were the questions and answers are already predefined, by examining the results you can aquire a general idea of the opinion of a certain population or group of people. The hard part here is to get sufficient responders so the data can represent the whole group at a significant level, and if you get enough responders, can you apply your results on other external groups or communities? These are all questions that I reflected upon during the course, I have acquired conclusions on some of them, and I are still trying to make sense of the rest.

After the theme of quantitative methods we entered the subject of qualitative and design research. The usage and application of qualitative methods directly caught my attention, since I see it as a brother to the application of quantitative methods. The usage of qualitative methods in itself didn't interest me very much, but its possibilities when combined with other methods seemed powerful, and that cought my attention.

Haibo Li stated that man spends to much time on solving a problem, when she should spend more time on defining the problem at hand. I thought that he had a good point, which got confirmed a week later when I heard a data analyst (Fredrik Göthner, an old Mediatechnology student) from Spotify talk about their approach to big data. He also mentioned the value of being able to define the problem at hand and breaking it up into smaller problems that can be treated and solved individually. Both Haibo and Fredrik talked about the power of defining problems, which was inspiring, since I have tried to apply that concept myself during my time as a student.

the combination of quantitative and qualitative methods, and the power of them together caught my interest during the course. A general way to put it is that quantitative research gives you the answer to “what is” while qualitative methods gives you the answer to “why it is”. For example, if you want to investigate the consumer behaviour of a specific geographic segment of consumers, you can convey a quantitative survey that you send out through different channels. The survey itself should be formed with specific questions and a couple of predefined answers, which in the end can be studied and analyzed to draw conclusions. This method tells you how your target group behaves, and you can see their consumer patterns and such, which later on can help you with whatever you use this data for, designing marketing concepts, putting a new product on the market, repositioning a retailer store or something else.

The problem with this method is that it does not give you a very good answer to why they behave like they do, and can be explain through a quantitative study. If you know the behaviour of a group you can form your quantitative questions to fit in, and give you the answers you seek. In this particular case I would construct a quantitative interview, and creat an interview group of a percentage of the segment which is able to represent its entirety. Through this application you break down the segment by asking them different questions which later on can paint the whole picture of their behaviour, and also explain why they behave like they do.

I think these methods will be very valuable to us as future engineers, not only in the construction and implementation of our bachelor and master thesis, but also later in our lives.