Imagine that you want to find out who your classmates plan to vote for. You’ll probably approach this by making a table with political parties and asking everyone you meet in school who they’ll vote for. You’ll put a check in the proper box and when you’ve got enough answers (let’s say one hundred), count up the checkmarks, put them in a graph, and you’re done.
You’ll take a similar approach when you want to find out how many stops it takes students to commute to school and what mode of transportation they use. You’ll probably divide students up by year and then query them. Each time, you’ll put a number into a table. Once you’ve got enough answers for each school year (let’s say at least ten), you’ll stop. Then you’ll count up the average for each year, and maybe for the whole school. Then you’ll know the result.
This kind of research is called quantitative. You can use this when you assume you know what you’re measuring well – for example, there is a pre-defined system to categorize political parties; for pocket money you can use an interval of numbers representing the amount in dollars or another currency (but, if one person received furs, the other salt, and another magic amulets, the whole thing would become extremely complicated!). You can do the same to measure students’ height, their parents’ education, or their favorite object.
This research is called quantitative because you need to know an answer from a sufficient number of people. No one can really tell you the exact number, but it should be roughly large enough so that another answer, whatever it is, won’t move the result greatly in one direction or another.
We usually use Excel (or more advanced tools) and statistics to process the data – here we’ve got great things like median, modus, dispersion, standard deviation, etc… The result of such research is usually a graph – something you can show to your classmates or the camera when you’re being interviewed on TV and say: “This is how things are.”
Now imagine that you want to find out why your classmates usually vote for a certain political party, what they see as the most important moment of their life, how they imagine the ideal teacher, or how your parents experienced their childhood under socialism. In these cases, no table can help you, because you don’t know what you’d write into it. While the previous method is very similar to the perspective of the natural sciences (which it has taken inspiration from), here we have to approach things differently – qualitatively.
Qualitative research usually uses interviews, or maybe an essay or video. Our goal is to use it to carefully analyze a given structure or pressing topics that we can continue to think about or make categories for quantitative research from them. With voting preferences, for example, you can carry out five or six interviews with voters and try to find out what important things are contained in them in regard to your topic. If the respondents agree at least a bit, you’ve more or less been successful.
Usually we try to approach research with as little bias as possible – for example, when respondents say that it’s important for them that the leader of a political party doesn’t speak a world language but his or her own strange, unique language, we might see this as eccentric, but it is still a relevant research result. This gives us one more important response – why do they think that, why is this particularly important to them and not the fact that he or she likes doughnuts or lives in an office space.
To process qualitative research, we usually use coding, i.e. symbols in a text that allow us to mark important passages and define them under certain shared terms. In an ideal situation, try to process transcriptions of interviews in the free CATMA. The result is usually a text with excerpts of what the respondents told you and how this can be understood.
While quantitative research will give you the answer to the question “how much?”, qualitative research answers the questions “how, what and why?”. Then you can select a suitable research path according to what you’re interested in. The division into two separate approaches doesn’t always have to be ideal, and you can have quantitative elements in your qualitative research and vice versa; this, however, gives us good guidance for understanding things at least at the beginning of research projects.