Understanding Research: Quantitative vs. Qualitative
When you think of research, what comes to mind? For many of you, it's probably a bunch of numbers that get analyzed in some way, and there's some sort of resulting number that tells us something. For a lot of research, this is generally true. But in psychology, many people make the obvious remark about this approach: numbers cannot perfectly represent human experiences. Unlike other types of measuring, like using a tape measure to find the length of something, psychology research is less precise. Many people use this fact as justification to call psychology a "soft" science. But that shows a misunderstanding of how psychology research is conducted. There are actually two forms of research that we use, and increasingly try to combine: quantitative and qualitative. Here, I want to briefly describe each.
Quantitative: The Numbers
The most common type of study you'll find in psychology is quantitative. In these studies, we are quantifying (i.e., measuring an amount using numbers) some set of variables. Each number is an estimate of what we are interested in, and we look across many different estimates to find patterns. For example, if a person reports frequently experiencing several symptoms of depression, we can estimate their level of depression to be relatively high. Responses to any one question are rarely very meaningful.
The main benefits to quantitative research are pretty simple: it's easier and there's a significance test. Many quantitative studies involve questionnaires that are given to participants, where the participants are asked to rate different things along scales. You've all seen these before, and have probably been frustrated with them. While they can be challenging for participants, they are nice and easy for the researcher: simply enter the numbers and run the analyses. (That's simplifying things a bit, but you get the idea). The analyses can be used to compare different numbers and to provide an estimate of whether the results are significant or not. Using this type of approach can also make it easier to ask a lot of questions very quickly.
As I mentioned, these types of studies can be frustrating for participants. The scales used for questions can be confusing, and you may feel as though none of the options are a good representation of reality for you. You may think you're between two options, or the wording isn't quite right for you. Yet you're forced to pick the "best" answer that's available. These questionnaires also limit what you're able to answer about. Even if they bring up something you feel is relevant and important, you can't provide that information unless there's a question asking about it. (And even if you write in the extra information, it will most likely be ignored).
Now, to be fair, there is a lot of work that goes into most of the measures we use in order to make them reliable (i.e., people answer them the same way consistently) and valid (i.e., they measure what we want them to measure). But they are still far from perfect, which is why we have a second approach that we can take (and use as a supplement to the quantitative approach).
Qualitative: The Descriptions
Qualitative research focuses more on the quality (so to speak) of something. That means a more personalized and subjective answer. Qualitative data is often collected in the form of interviews (one-on-one or in a group setting). These interviews are often transcribed, and different themes are highlighted by the researcher.
Many participants enjoy qualitative research studies because they have more flexibility in the responses they give. If someone asks about family meals in your home, you can talk about the type of food you often prepare, what roles people have, how it can be difficult to get your teenager to be home for dinner, and so on. Not only can you answer in a way that is more open, but it gives the researcher more details than something like "How many days per week do you eat meals together as a family?"
The results also give researchers a much richer understanding of complex things. For example, community-based research often uses qualitative analyses because there are so many things about a community that cannot be measured, but can be discussed in an interview. By speaking with members of a community, a researcher can begin to understand how that community works.
Of course, qualitative research does not come without some drawbacks. First, it is generally much more time-consuming than quantitative research. There are more steps to the process, things need to be double-coded to reduce bias from the researcher, it often takes many passes through the data to identify the relevant information, and so on. Plus, there are a wide range of different approaches that can be taken to conduct qualitative research, which researchers need to be familiar with.
(But please keep in mind, there are many checks in place to prevent a researcher from simply picking the information that makes the claim they want it to; it still happens, but qualitative studies are generally rigorous and good sources of scientific information).
In fact, doing purely qualitative research is so difficult that there's a tendency for researchers to start making it more quantitative. For example, themes may be represented by how often they are brought up, which is a number that can be used for quantitative analyses. Some try to argue that this is a combination of the two approaches, but really it's converting one into another.
Plus, the extra flexibility in qualitative studies can arguably cause a problem. We know from a lot of research that what we think is happening (and thus how we'll talk about something in an interview) is not necessarily what is actually happening. That is, people may talk about violence in their community has being related to one thing, when really there are other factors playing a role that most people don't realize. (And to be fair, you can make an argument that quantitative studies that rely on self-reports from participants have the same challenge). So there can be a question of how accurately the data end up representing the real world.
In the end, both approaches have their benefits. Quantitative studies are more rigid and can estimate whether or not something is meaningfully significant. Yet qualitative studies can better capture the complexities of reality for people. As we move forward, I imagine researchers will continue to try combining these two approaches in more useful and meaningful ways.
Unfortunately, things like Big Data are arguably trying to use quantitative approaches to do the work of a qualitative approach. This may slow down the process of integrating qualitative approaches, but I expect they will never fully replace them.
So if you find yourself thinking psychology research is only about the numbers, rest assured that quantitative analyses aren't our only approach.
Hopefully this has been a helpful introduction to the differences between quantitative and qualitative research approaches. If if you have thoughts and/or questions, feel free to leave them in the comments!