Getting Started with Research – Basic Knowledge on Social Science Research Methods


Choosing the correct method depends on your research question and your aim. Do you want to describe something, explore new questions or do you want to confirm a hypothesis? Answering this question first will be of key importance when choosing methods for your research project. Another factor to consider and heavily linked to the first question raised above, is asking yourself what kind of research you want to carry out. The question then is whether you want to use a deductive or an inductive approach.

Deductive: This term simply means that you start with a theory from which you develop a hypothesis, based on which you choose your research methods. In your research you then seek to confirm or disprove your hypothesis. This research approach is associated with quantitative data.

Inductive: In contrast to deductive reasoning, using an inductive approach means that you start with observations without having either a hypothesis or a theory guiding your research. The goal of inductive research is to let the hypothesis and theory emerge through the research you do. The idea is that you start your research with a clean slate in terms of theoretical expectations. This is of course very difficult, if not impossible, as we are always influenced by the things we experience and we often can’t help but have preconceived ideas of things, people and social phenomena surrounding us. Nonetheless, the challenge is to block this out as much as possible and to let the theory emerge from the observations you make. This kind of approach is strongly associated with qualitative data.

As you can guess from the above differentiation, often there lies a large ideological gulf between these two approaches that is also reflected in the question what kind of data you should collect. The key difference lies in quantitative and qualitative data.

Quantitative Data: Basically, quantitative data are numerical data that allow you to measure and quantify things and to test hypotheses. Most typically you will collect quantitative data through questionnaires with closed questions and scales. This means that the participant can only select pre-given answers which have an assigned numerical value for later analysis. Alternatively, they may be asked to select a numerical value on a scale that represents a certain answer e.g. Strongly disagree =1, Strongly agree = 5. These types of scales are called Likert Scales. Quantitative data have long been seen as the only valid sort of data in scientific research, but this perception has been eroded as the collection of qualitative data has become more widely accepted and used, especially among social scientists.

Qualitative Data: When you collect qualitative data you try to understand meanings and interpretations. The idea is to collect narratives of research participants, to be more detailed and holistic, and to account for contextual circumstances. This kind of data may be made up of words, direct observations, videos, texts, pictures etc. and will be most typically collected using interviews, focus groups or observational techniques.

The distinction between quantitative and qualitative data seems very clear and hard, yet numerical data always has a qualitative and subjective element to it and qualitative data can be quantified as well, e.g. through textual analysis that counts words. If you are interested in a more detailed discussion of this ideological divide, have a look here:

Mixed Methods

In fact overcoming this gulf can actually be very fruitful and more and more researchers, including myself, discover the beauty of using mixed methods. A mixed methods approach allows you to collect both quantitative and qualitative data and benefits from the advantages of both types of data while offsetting, at least to some extent, the disadvantages of either. In addition it makes your final thesis and interpretations stronger as you rely on multiple sources of evidence, not just a single one. An often used term to describe this advantage is data triangulation.

The Difference between Methods and Methodology

This question is another thing that bothers many students that are confronted with it for the first time and often teachers fail to give a simple answer to this question. The problem is it is not easy to answer this question in simple terms but I will try to answer it in the way I have learned to understand the difference over the years. For me Methodology is the overall approach I am using to develop a research project and Methods are the techniques I utilize as part of this approach. Methodology requires you to understand your research aim and question first and to have a good understanding of the different ideologies that may guide this research. This understanding will then allow you to choose the best methods to accomplish your goals.

So in the most basic and reductionist sense:

Methodology = the why

Methods = the how

The above discussed terms are only a starting point in understanding the research process in the social sciences but sometimes just getting a basic understanding of some complicated sounding words can open a new world of exciting research opportunities for you.


Dealing With and Visualizing Data – A Free Resource to Give You an Edge


When studying at university at one point many students will have to deal with data in one form or another. Especially if you are a business or social science student, statistics and data visualization will play a role in your undergraduate and postgraduate life at one point or another. Usually, you will be given access to a statistical software package such as SPSS. While these programs are really good to help you analyze data quickly and effectively, they are no good at visualizing your results. What I have done in the past, and probably many others finding SPSS data visualizations horrible, I copied the relevant data output to Microsoft Excel and built my graphs there. Now, though I have spent many, many years using excel and, believe it or not, I love it (I think it is really powerful in many sorts of ways), Excel is also no particularly advanced program for making nice data visualizations. It gives you more flexibility than for instance SPSS, but that’s it.

So recently I came across Tableau. What is Tableau: It is a really nice piece of software that aims at making data visualization visually attractive without you having to learn how to use costly, professional design software to make elaborate charts and graphs. And what’s more, they offer a one year license for free to all students with a valid university email address.

Now, I don’t want to be seen as advertising any particular software here, but as I said it is free; there is no cost attached to it if you are a student. So why not sharing this knowledge! And when you look at what has already been created with Tableau, you sure want to give it at least a try.

I have downloaded tableau a few months ago myself and I have already created a few nice charts with data from my PhD project. So I have already gained some knowledge on what can be done with it and what can’t. From these experiences I can tell you, it is not 100% self-explanatory to use it and you need to experiment a little and try different things out; but after a while you get the hang of it and it starts to become a lot more powerful. In this way it is similar to Excel. As I said, I have used Excel extensively over the last years but it also took me a lot of practice to get to my current level of expertise in using it. The good thing is that the Tableau employees know this and they have provided quite a lot of tutorials that show you how to use their software. Again, the available tutorials are by no means comparable to the amount of support available for Excel or SPSS but they are pretty good nonetheless. Of course the first time you try to play with your data in Tableau after having watched the first tutorial you think, I haven’t got a clue how they did it in the tutorial (at least that’s what I thought…) but the longer you try the more knowledge you’ll gain and the better you’ll get at it.

So if you have to deal with a lot of data, I would encourage you to download this software and try it out. It may give you an edge in coursework as well as in later job searches where differentiating skills are required.