The data I collect can be one of two things:
It could be categories, or it could be numbers.
Categorical data, or qualitative data, is
data where it might be red, green, blue, or
Ford, Holden, Toyota. Categories.
Numerical data, or quantitative data, is where
there’s a number involved. There were 17
cars, there were 5 people. The height was
6.7 metres, the length was… Numbers.
We can divide each of these kinds of data
another step.
Let’s start with Categorical data.
Nominal data is data where the categories
are just named. “Nominal” means names.
Red, green, blue. Apples, oranges, bananas,
kiwifruit.
Ordinal is where there’s categories but
there’s some kind of order. Big, medium,
small. Best, preferred, acceptable, maybe,
no way! Although they’re categories, there’s
an implied order to them.
Both of these kinds of data are in categories.
Nominal is when they’re just named, ordinal
is when there’s some kind of (implied) order.
Numerical data we also break up into two kinds.
Discrete data is where only particular values
are allowed. Usually only whole numbers, like
“how many cars…?” or “number of children”.
Continuous data is where any value (in range)
is allowed. Mass of the cars, children’s
heights. Any numeric value is possible, so
that’s continuous.
There are sometimes tricky cases. Take shoe
sizes for example. Although I can have 4,
4½, 5, 5½, and so on—although I can have
halves, I can’t have any other fractions.
I can’t talk about a shoe size of 5.1473.
So only particular values are allowed. That’s
discrete. But if I measure the actual length
of the shoe, that’s going to be continuous,
because it’s going to be a precise number
of millimetres or whatever; even two shoes
of the same “size” will have different
lengths.
