Qualitative vs Quantitative Research 101

A Plain-Language Explanation (With Examples)

By: Kerryn Warren (PhD, MSc, BSc) | June 2020

So, it’s time to decide what type of research approach you’re going to use – qualitative or quantitative. And, chances are, you want to choose the one that fills you with the least amount of dread. The engineers may be keen on quantitative methods because they loathe interacting with human beings and dealing with the “soft” stuff and are far more comfortable with numbers and algorithms. On the other side, the anthropologists are probably more keen on qualitative methods because they literally have the opposite fears.

However, when justifying your research, “being afraid” is not a good basis for decision making. Your methodology needs to be informed by your research aims and objectives, not your comfort zone. Plus, it’s quite common that the approach you feared (whether qualitative or quantitative) is actually not that big a deal. Research methods can be learnt (usually a lot faster than you think) and software reduces a lot of the complexity of both quantitative and qualitative data analysis. Conversely, choosing the wrong approach and trying to fit a square peg into a round hole is going to create a lot more pain.

In this post, I’ll explain the qualitative vs quantitative choice in straightforward, plain language with loads of examples. This won’t make you an expert in either, but it should give you a good enough “big picture” understanding so that you can make the right methodological decision for your research.

Qualitative Research 101: The Basics

The bathwater is hot.

Let us unpack that a bit. What does that sentence mean? And is it useful?

The answer is: well, it depends. If you’re wanting to know the exact temperature of the bath, then you’re out of luck. But, if you’re wanting to know how someone perceives the temperature of the bathwater, then that sentence can tell you quite a bit if you wear your qualitative hat.

Many a husband and wife have never enjoyed a bath together because of their strongly held, relationship-destroying perceptions of water temperature (or, so I’m told). And while divorce rates due to differences in water-temperature perception would belong more comfortably in “quantitative research”, analyses of the inevitable arguments and disagreements around water temperature belong snugly in the domain of “qualitative research”. This is because qualitative research helps you understand people’s perceptions and experiences by systematically coding and analysing the data.

With qualitative research, those heated disagreements (excuse the pun) may be analysed in several ways. From interviews to focus groups to direct observation (ideally outside the bathroom, of course). You, as the researcher, could be interested in how the disagreement unfolds, or the emotive language used in the exchange. You might not even be interested in the words at all, but in the body language of someone who has been forced one too many times into (what they believe) was scalding hot water during what should have been a romantic evening. All of these “softer” aspects can be better understood with qualitative research.

In this way, qualitative research can be incredibly rich and detailed, and is often used as a basis to formulate theories and identify patterns. In other words, it’s great for exploratory research (for example, where your objective is to explore what people think or feel), as opposed to confirmatory research (for example, where your objective is to test a hypothesis). Qualitative research is used to understand human perception, world view and the way we describe our experiences. It’s about exploring and understanding a broad question, often with very few preconceived ideas as to what we may find.

But that’s not the only way to analyse bathwater, of course…

Quantitative Research 101: The Basics

The bathwater is 45 degrees Celsius.

Now, what does this mean? How can this be used?

I was once told by someone to whom I am definitely not married that he takes regular cold showers. As a person who is terrified of anything that isn’t body temperature or above, this seemed outright ludicrous. But this raises a question: what is the perfect temperature for a bath? Or at least, what is the temperature of people’s baths more broadly? (Assuming, of course, that they are bathing in water that is ideal to them). To answer this question, you need to now put on your quantitative hat.

If we were to ask 100 people to measure the temperature of their bathwater over the course of a week, we could get the average temperature for each person. Say, for instance, that Jane averages at around 46.3°C. And Billy averages around 42°C. A couple of people may like the unnatural chill of 30°C on the average weekday. And there will be a few of those striving for the 48°C that is apparently the legal limit in England (now, there’s a useless fact for you).

With a quantitative approach, this data can be analysed in heaps of ways. We could, for example, analyse these numbers to find the average temperature, or look to see how much these temperatures vary. We could see if there are significant differences in ideal water temperature between the sexes, or if there is some relationship between ideal bath water temperature and age! We could pop this information onto colourful, vibrant graphs, and use fancy words like “significant”, “correlation” and “eigenvalues”. The opportunities for nerding out are endless…

In this way, quantitative research often involves coming into your research with some level of understanding or expectation regarding the outcome, usually in the form of a hypothesis that you want to test. For example:

Hypothesis: Men prefer bathing in lower temperature water than women do.

This hypothesis can then be tested using statistical analysis. The data may suggest that the hypothesis is sound, or it may reveal that there are some nuances regarding people’s preferences. For example, men may enjoy a hotter bath on certain days.

So, as you can see, qualitative and quantitative research each have their own purpose and function. They are, quite simply, different tools for different jobs.

Qualitative vs Quantitative Research:
Which one should you use?

And here I become annoyingly vague again. The answer: it depends. As I alluded to earlier, your choice of research approach depends on what you’re trying to achieve with your research. 

If you want to understand a situation with richness and depth, and you don’t have firm expectations regarding what you might find, you’ll likely adopt a qualitative research approach. In other words, if you’re starting on a clean slate and trying to build up a theory (which might later be tested), qualitative research probably makes sense for you.

On the other hand, if you need to test an already-theorised hypothesis, or want to measure and describe something numerically, a quantitative approach will probably be best. For example, you may want to quantitatively test a theory (or even just a hypothesis) that was developed using qualitative research.

Basically, this means that your research approach should be chosen based on your broader research aims, objectives and research questions. If your research is exploratory and you’re unsure what findings may emerge, qualitative research allows you to have open-ended questions and lets people and subjects speak, in some ways, for themselves. Quantitative questions, on the other hand, will not. They’ll often be pre-categorised, or allow you to insert a numeric response. Anything that requires measurement, using a scale, machine or… a thermometer… is going to need a quantitative method.

Let’s look at an example.

Say you want to ask people about their bath water temperature preferences. There are many ways you can do this, using a survey or a questionnaire – here are 3 potential options:

1. How do you feel about your spouse’s bath water temperature preference? (Qualitative. This open-ended question leaves a lot of space so that the respondent can rant in an adequate manner).
2. What is your preferred bath water temperature? (This one’s tricky because most people don’t know or won’t have a thermometer, but this is a quantitative question with a directly numerical answer).
3. Most people who have commented on your bath water temperature have said the following (choose most relevant): It’s too hot. It’s just right. It’s too cold. (Quantitative, because you can add up the number of people who responded in each way and compare them).

The answers provided can be used in a myriad of ways, but, while quantitative responses are easily summarised through counting or calculations, categorised and visualised, qualitative responses need a lot of thought and are re-packaged in a way that tries not to lose too much meaning.

Qualitative vs Quantitative Research:
Data collection and analysis

The approach to collecting and analysing data differs quite a bit between qualitative and quantitative research.

A qualitative research approach often has a small sample size (i.e. a small number of people researched) since each respondent will provide you with pages and pages of information in the form of interview answers or observations. In our water perception analysis, it would be super tedious to watch the arguments of 50 couples unfold in front of us! But 6-10 would be manageable and would likely provide us with interesting insight into the great bathwater debate.

To sum it up, data collection in qualitative research involves relatively small sample sizes but rich and detailed data.

On the other side, quantitative research relies heavily on the ability to gather data from a large sample and use it to explain a far larger population (this is called “generalisability”). In our bathwater analysis, we would need data from hundreds of people for us to be able to make a universal statement (i.e. to generalise), and at least a few dozen to be able to identify a potential pattern. In terms of data collection, we’d probably use a more scalable tool such as an online survey to gather comparatively basic data.

So, compared to qualitative research, data collection for quantitative research involves large sample sizes but relatively basic data.

Both research approaches use analyses that allow you to explain, describe and compare the things that you are interested in. While qualitative research does this through an analysis of words, texts and explanations, quantitative research does this through reducing your data into numerical form or into graphs.

There are dozens of potential analyses which each uses. For example, qualitative analysis might look at the narration (the lamenting story of love lost through irreconcilable water toleration differences), or the content directly (the words of blame, heat and irritation used in an interview). Quantitative analysis may involve simple calculations for averages, or it might involve more sophisticated analysis that assesses the relationships between two or more variables (for example, personality type and likelihood to commit a hot water-induced crime). We discuss the many analysis options other blog posts, so I won’t bore you with the details here.

Qualitative vs Quantitative Research:
The pros & cons on both sides

Quantitative and qualitative research fundamentally ask different kinds of questions and often have different broader research intentions. As I said earlier, they are different tools for different jobs – so we can’t really pit them off against each other. Regardless, they still each have their pros and cons.

Let’s start with qualitative “pros”

Qualitative research allows for richer, more insightful (and sometimes unexpected) results. This is often what’s needed when we want to dive deeper into a research question. When we want to find out what and how people are thinking and feeling, qualitative is the tool for the job. It’s also important research when it comes to discovery and exploration when you don’t quite know what you are looking for. Qualitative research adds meat to our understanding of the world and is what you’ll use when trying to develop theories.

Qualitative research can be used to explain previously observed phenomena, providing insights that are outside of the bounds of quantitative research, and explaining what is being or has been previously observed. For example, interviewing someone on their cold-bath-induced rage can help flesh out some of the finer (and often lost) details of a research area. We might, for example, learn that some respondents link their bath time experience to childhood memories where hot water was an out of reach luxury. This is something that would never get picked up using a quantitative approach.

There are also a bunch of practical pros to qualitative research. A small sample size means that the researcher can be more selective about who they are approaching. Linked to this is affordability. Unless you have to fork out huge expenses to observe the hunting strategies of the Hadza in Tanzania, then qualitative research often requires less sophisticated and expensive equipment for data collection and analysis.

Qualitative research also has its “cons”:

A small sample size means that the observations made might not be more broadly applicable. This makes it difficult to repeat a study and get similar results. For instance, what if the people you initially interviewed just happened to be those who are especially passionate about bathwater. What if one of your eight interviews was with someone so enraged by a previous experience of being run a cold bath that she dedicated an entire blog post to using this obscure and ridiculous example?

But sample is only one caveat to this research. A researcher’s bias in analysing the data can have a profound effect on the interpretation of said data. In this way, the researcher themselves can limit their own research. For instance, what if they didn’t think to ask a very important or cornerstone question because of previously held prejudices against the person they are interviewing?

Adding to this, researcher inexperience is an additional limitation. Interviewing and observing are skills honed in over time. If the qualitative researcher is not aware of their own biases and limitations, both in the data collection and analysis phase, this could make their research very difficult to replicate, and the theories or frameworks they use highly problematic.

Qualitative research takes a long time to collect and analyse data from a single source. This is often one of the reasons sample sizes are pretty small. That one hour interview? You are probably going to need to listen to it a half a dozen times. And read the recorded transcript of it a half a dozen more. Then take bits and pieces of the interview and reformulate and categorize it, along with the rest of the interviews.

Now let’s turn to quantitative “pros”:

Even simple quantitative techniques can visually and descriptively support or reject assumptions or hypotheses. Want to know the percentage of women who are tired of cold water baths? Boom! Here is the percentage, and a pie chart. And the pie chart is a picture of a real pie in order to placate the hungry, angry mob of cold-water haters.

Quantitative research is respected as being objective and viable. This is useful for supporting or enforcing public opinion and national policy. And if the analytical route doesn’t work, the remainder of the pie can be thrown at politicians who try to enforce maximum bath water temperature standards. Clear, simple, and universally acknowledged. Adding to this, large sample sizes, calculations of significance and half-eaten pies, don’t only tell you WHAT is happening in your data, but the likelihood that what you are seeing is real and repeatable in future research. This is an important cornerstone of the scientific method.

Quantitative research can be pretty fast. The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average. In fact, if you are really fancy, you can code and automate your analyses as your data comes in! This means that you don’t necessarily have to worry about including a long analysis period into your research time.

Lastly – sometimes, not always, quantitative research may ensure a greater level of anonymity, which is an important ethical consideration. A survey may seem less personally invasive than an interview, for instance, and this could potentially also lead to greater honesty. Of course, this isn’t always the case. Without a sufficient sample size, respondents can still worry about anonymity – for example, a survey within a small department.

But there are also quantitative “cons”:

Quantitative research can be comparatively reductive – in other words, it can lead to an oversimplification of a situation. Because quantitative analysis often focuses on the averages and the general relationships between variables, it tends to ignore the outliers. Why is that one person having an ice bath once a week? With quantitative research, you might never know…

It requires large sample sizes to be used meaningfully. In order to claim that your data and results are meaningful regarding the population you are studying, you need to have a pretty chunky dataset. You need large numbers to achieve “statistical power” and “statistically significant” results – often those large sample sizes are difficult to achieve, especially for budgetless or self-funded research such as a Masters dissertation or thesis.

Quantitative techniques require a bit of practice and understanding (often more understanding than most people who use them have). And not just to do, but also to read and interpret what others have done, and spot the potential flaws in their research design (and your own). If you come from a statistics background, this won’t be a problem – but most students don’t have this luxury.

Finally, because of the assumption of objectivity (“it must be true because its numbers”), quantitative researchers are less likely to interrogate and be explicit about their own biases in their research. Sample selection, the kinds of questions asked, and the method of analysis are all incredibly important choices, but they tend to not be given as much attention by researchers, exactly because of the assumption of objectivity.

Mixed methods: a happy medium?

Some of the richest research I’ve seen involved a mix of qualitative and quantitative research. Quantitative research allowed the researcher to paint “birds-eye view” of the issue or topic, while qualitative research enabled a richer understanding. This is the essence of mixed-methods research – it tries to achieve the best of both worlds.

In practical terms, this can take place by having open-ended questions as a part of your research survey. It can happen by having a qualitative separate section (like several interviews) to your otherwise quantitative research (an initial survey, from which, you could invite specific interviewees). Maybe it requires observations: some of which you expect to see, and can easily record, classify and quantify, and some of which are novel, and require deeper description.

A word of warning – just like with choosing a qualitative or quantitative research project, mixed methods should be chosen purposefully, where the research aims, objectives and research questions drive the method chosen. Don’t choose a mixed-methods approach just because you’re unsure of whether to use quantitative or qualitative research. Pulling off mixed methods research well is not an easy task, so approach with caution!

Recap: Qualitative vs Quantitative Research

So, just to recap what we have learned in this post about the great qual vs quant debate:

1. Qualitative research is ideal for research which is exploratory in nature (e.g. formulating a theory or hypothesis), whereas quantitative research lends itself to research which is more confirmatory (e.g. hypothesis testing)
2. Qualitative research uses data in the form of words, phrases, descriptions or ideas. It is time-consuming and therefore only has a small sample size.
3. Quantitative research uses data in the form of numbers and can be visualised in the form of graphs. It requires large sample sizes to be meaningful.
4. Your choice in methodology should have more to do with the kind of question you are asking than your fears or previously-held assumptions.
5. Mixed methods can be a happy medium, but should be used purposefully.
6. Bathwater temperature is a contentious and severely under-studied research topic.