“Quant tells us what. Qual tells us why.”
This oversimplification regularly gets teams into trouble.
It suggests that quant and qual are simple, single dimensional measurements.
And it suggests they’re somehow to be considered separately.
Neither suggestion is correct.
This oversimplification regularly gets teams into trouble.
It suggests that quant and qual are simple, single dimensional measurements.
And it suggests they’re somehow to be considered separately.
Neither suggestion is correct.
Qualitative data comes in all shapes and sizes, as does quantitative data.
For qual data, we have user's problems, their needs, their journeys, their outcomes.
For quant, we have success rates, problem rates, attitudes, costs.
For qual data, we have user's problems, their needs, their journeys, their outcomes.
For quant, we have success rates, problem rates, attitudes, costs.
We mix and match. More importantly, we combine them to tell a story the informs the decisions we have before us.
The richer the data, the better the story.
And the better our decisions will be.
The richer the data, the better the story.
And the better our decisions will be.
To suggest that quant is only numbers and qual is only non-numeric data would be wrong.
Measurements can be collected in a myriad of forms and they aren't so distinct.
Measurements can be collected in a myriad of forms and they aren't so distinct.
For example, if I ask someone if they're happy or unhappy, that seems like qual data.
But if I ask them to rate their happiness on a 3-point scale, I now have what seems like quant data.
But if I ask them to rate their happiness on a 3-point scale, I now have what seems like quant data.
Yet, someone who rates their happiness a 2 isn't twice as happy as someone who rates it a 1.
What do the numbers mean? Nothing more than they did when it was qual data.
What do the numbers mean? Nothing more than they did when it was qual data.
Thoroughly-researched quantitative data will go beyond the "what."
It will show us the rich variations we see in our user's behaviors. It can combine those with specific attitudes and intentions. It can show strong moments of frustration and delight.
It will show us the rich variations we see in our user's behaviors. It can combine those with specific attitudes and intentions. It can show strong moments of frustration and delight.
Similarly, thoroughly-research qualitative data definitely goes beyond the "why."
It shows us what's happening beyond clicks and times. It tells us the stories of what happens on the other side of the screen, that we can't see in our logs.
It shows us what's happening beyond clicks and times. It tells us the stories of what happens on the other side of the screen, that we can't see in our logs.
This separation of quant and qual has created a rift amongst UX professionals, where we've divided up the work between people who do research that's qualitative and people who do research that's quantitative.
Those two groups have different methods, language, and results.
Those two groups have different methods, language, and results.
And that's where the big problem lies. Because they shouldn't be separate. They need to be combined with a common language.
We can easily switch from one to the other, depending on the story we want to tell and how we want to tell it.
We can easily switch from one to the other, depending on the story we want to tell and how we want to tell it.
That means that every UX professional needs skills, knowledge, and experience in both kinds of research.
Storytelling is at the core of great user experience work.
We can't tell persuasive stories without data.
It's not about separating the what from the why at all.
Storytelling is at the core of great user experience work.
We can't tell persuasive stories without data.
It's not about separating the what from the why at all.
