Hi, I'm Jen. Welcome! Today we're talking about quantitative versus qualitative
data. These are two terms that people often find really confusing so we're
going to break it down. We'll look at what the differences are and then talk about
when you would use each in your research or analysis process. Data can generally
be divided into two main types - quantitative data and qualitative data.
The root of each of these words really tells you what it is. Quantitative
data talks about quantities or things that can be measured numerically. It's
often things that can be either whole numbers or can be measured on a sliding
scale, but they naturally have a fixed number associated with them. Qualitative
data on the other hand talks about a quality or characteristic that describes
something. Quantitative data is used when you're trying to either quantify a
problem or answer a how many or how much type of question. This type of data can
be counted or measured on a numeric scale. A few examples of quantitative
data would include your height or your weight or the number of children in a
family. These are things that we naturally describe with a number.
Quantitative data has two subsets as well so there's two different ways we
can describe quantitative data. It can be discrete or it can be continuous.
Discrete data has fixed values that can happen. For instance, if we talk about the
number of children in a family, you don't hear that there's two and a half
children. There's two children or there's three children.
This is discrete data. There's a limited number of options for what it can be.
This is also a type of data that normally can't be defined any more
specifically. So saying 2.0 children is no more specific than saying two
children. On the other hand, continuous data is again still measurable data, but
it can continue to be divided or refined or specified further. If we take a
person's weight or height, we can continue to get more and more
microscopic with the level if we a scale or a measurement device that
would get more specific. Someone could weigh 100 pounds or it
could be 100.1 or 100.15, 100.159 and it can keep getting
more specific as long as your measurement tool is more specific. At
some point there's going to be a lot of zeros on the end, but in most cases it
can continue to divide further and further. That's continuous data.
Qualitative data describes or characterizes the thing that you're
gathering information about. Think of qualitative data as descriptions where
you would normally provide a word and not a number. Eye color or hair color would
be qualitative data. There is a lot more subjectivity. You could create a
definition of this is what brown hair means, but we all know brown hair can
have a pretty wide spectrum to it. You could get more specific and say it's a
blondish-brown or it's a dark brown but these are still fairly subjective terms.
The same with eye color. Maybe you have blue eyes or maybe you have blue-grey
eyes or blue-green eyes. There's a lot of different options here for how to
describe eye color. Qualitative data is often more difficult
to measure and analyze because it is more subjective. This doesn't mean you
can't analyze qualitative data though. We'll talk a lot more in depth in next
week's video about how to do qualitative data analysis. In short, the way this is
done is assigning some sort of code or standard to the qualitative data.
Qualitative data can be analyzed for meaning or patterns in the information
through the use of this coding process. Those are the key differences between
quantitative versus qualitative data. Let me know if you have any questions about
these types of data in the comments. I'd be happy to answer them. Thanks so much
for watching and I'll see you back next week to talk about qualitative data
analysis.