- In this course, we'll take a look
at two more research methods:
qualitative research, and quantitative research.
These two methods define the type of data
that's being collected,
whether it's qualitative, a subjective belief,
or quantitative, an objective measurement.
Quantitative research measures quantifiable things:
measurements, quantities, and variables.
These are all objective data,
and objective means that this type of data
is difficult to argue with.
For example, the distance from the train station
to the town center is 1.5 miles,
56% of people leaving the station
take a cab to their next destination,
and the average amount of money spent on a ride is $6.57.
These are all measurements, or quantities,
and are all difficult to argue with, right?
This is objective data,
so why do you think it's important
to have objective research data?
First, it helps us, the designers, to make smarter,
less opinion-based decisions when we're working.
It also makes it much easier to explain
how our decisions led us to the final solution.
Here's what I mean.
Consider this statement,
which is an opinion made by a designer:
"I chose this typeface
"because it looks better than the old one."
Now, I could easily respond with,
"Well, I don't think it looks good at all.
"In fact, I think it should be this typeface instead."
And all of a sudden, we're lost in a discussion
about our own typeface preferences.
Now, listen to it when we pair
that decision with quantitative data:
"We chose this typeface because 87%
"of people younger than 30
"responded positively to the historical side of our brand.
"We feel this typeface communicates a modern appeal,
"while still referencing that history."
That sounds much better, right?
So, the big idea is to use quantitative data
to support your decisions,
and present objective thinking in your design.
Quantitative data can also be helpful
in the beginning of a project to answer early questions.
Let's use the example of designing a new brand identity.
You, the designer, believe that the brand feels outdated,
and because of this, people have stopped responding to it.
But this is an assumption.
You think it's true,
but you have little evidence to back it up.
So, you send out a survey,
and find out that your assumption was correct,
that 75% of people feel the brand is outdated.
Now, earlier, I said that quantitative research
is difficult to argue with.
However, it can be argued with
by questioning how accurately the data was collected.
For instance, let's consider that 75% statistic
of people who feel the brand is outdated.
Perhaps, you only asked young people what they thought.
This group of people is probably
much more likely to say it's outdated
because of their age and personal taste.
The audience essentially swayed the results.
We'll talk more about how to avoid this,
and how to use research tools,
such as surveys, interviews,
and observational research properly, in Chapter 4.
Now, let's consider qualitative research.
It measures subjective data, such as beliefs and opinions.
The key thing to remember with qualitative research
is that it is subjective,
meaning it can be more difficult to defend.
For example, "I'm taking the train
"because the town center is too far
"from the airport to pay for a cab."
This information is subjective.
It's based on the opinion and belief
of the person being asked.
So, why would we bother with qualitative research?
Opinions and beliefs do something
that quantitative data cannot;
they begin to explain why.
And understanding why something is true
is very useful in design.
Let's go back to our brand identity example,
and consider some qualitative research
gathered through personal interviews.
"I've always liked Company X.
"I've been shopping there for years
"because I really feel like they care about you."
Participant A, 46 years old.
"I prefer shopping at Company X
"because they're part of our local community,
"and I believe in supporting small business."
Participant B, 42 years old.
"I think that Company X is overpriced,
and I'd rather shop at a discount retail chain."
Participant C, 28 years old.
So, what can we begin to assume
from these three short pieces of information?
Company X has loyalty with an older audience,
who believes in supporting the local community,
and they've not connected with a potential younger market
that is more interested in saving money
at a discount retail chain.
Qualitative research is subjective.
For instance, the statement that the 28-year-old made
that Company X is expensive
has a lot of variables to it.
How many other people felt the same way?
What was the average age of the person asked?
What was their average income?
The bottom line is,
is that there's a lot that can be argued with here.
Now, if that statement were backed up
with other interview quotes, observations,
or some kind of quantitative data,
then we'd have reason to defend it,
and act on it.
In best practice, both quantitative
and qualitative research are used together
to support each other's findings.
A quantitative piece of data should frame the idea,
then a series of subjective,
qualitative observations may begin to provide insight,
or explanation behind it.