Qual vs. Quant, and why Both is Better

Provide a fuller picture by mixing your methods

Do you sometimes struggle to explain why you need both quantitative and qualitative data? Or are you the one asking your research partner this question? Read on for a quick illustration (literally) of how these two types of data compliment one another and give a richer impression of your customers or users, their wants and needs, and their overall journey.

What is quantitative or qualitative data?

We’ll start with some definitions. A small detail first: “data” is the plural form of the singular “datum,” but people often use the term data whether they mean it to be plural or not. I won’t judge you either way :-)

• Quantitative data are represented numerically. This includes anything you can count, measure, or otherwise assign a numerical value to. If you ask someone for a 1–5 rating of something, you’re collecting quantitative data. If you’re counting how many people did X thing, that’s also quantitative. Lengths and temperatures? Those are quantitative data as well.
• Qualitative data are represented non-numerically. They most commonly express information and concepts through words. A person describing how they feel about an experience is qualitative data. Photographs, drawings, and videos also contain qualitative data. Rather than counting this data it is often analyzed by categorizing it using data codes or tags. Sometimes people don’t think this largely word-based information is data, but it is.

For simplicity these are often referred to as quant and qual, or some people say QT and QL.

How quant and qual work together

To demonstrate why both forms of data are useful I use the analogy of a picture. I like to think of quantitative data as the outline of the picture. It defines and outlines the boundaries. Qualitative data on the other hand provide the color. It shows nuance and makes the image more lively. Together you have a complete picture, like the cute owl at the top of this post.

You might feel like pointing out that in fact you can create an identifiable image using just one or the other. Or, to apply the analogy to your work as a researcher, if you wanted to show your stakeholders what was going on you could use just the quant (the outline) or just the qual (the color). And that’s true. Even with my cute owl.

You can tell it’s an owl in either version. However, it’s not realistically possible to ever have all the quantitative data or all the qualitative data that could describe an area of inquiry (aka your research topic). With incomplete quant you’d only ever get a partial outline.

Because you already knew this was an owl, you might still “see” the owl — but it’s no longer as easily identified. It could maybe be a cat? Or some kind of animal-inspired mascot. And should it have a second ear or has this creature actually lost one? The quantitative data that was available to be collected leaves some gaps and we’re left trying to connect those lines using our best guesses.

The same thing happens with qualitative data. You’d only able to gather some of the color.

Here you have swathes of color, and see some basic shapes, but you can’t be sure what this image represents. This isn’t a fault of qualitative data collection. Or of quantitative. It’s simply not realistic to expect you could access every bit of data.

This is exactly why combining quant and qual is so powerful. Let’s merge our two partial images.

Our color and our outline (our qual and quant) compliment one another. Each one fills in some spaces that we were blind to in the other, and in cases where both are present they add definition or nuance.

Providing definition and nuance

A healthy project both defines the boundaries and fills in the lines — each as much as is needed to make the picture identifiable, or actionable. In a real world research engagement you’ll need to make some choices on how much research is possible but this analogy demonstrates why it’s valuable to include a mix of quant and qual — especially when you know you won’t be able to collect a full picture.