Learning Statistics with R - A tutorial for psychology students and other beginners, 2018a

x class(x)
[1] "logical"
> x class(x)
[1] "numeric"
Exciting, no?
4.7
Factors
Okay, it’s time to start introducing some of the data types that are somewhat more specific to statistics. If
you remember back to Chapter 2, when we assign numbers to possible outcomes, these numbers can mean
quite different things depending on what kind of variable we are attempting to measure. In particular,
we commonly make the distinction between nominal, ordinal, interval and ratio scale data. How do
we capture this distinction in R? Currently, we only seem to have a single numeric data type. That’s
probably not going to be enough, is it?
A little thought suggests that the numeric variable class in R is perfectly suited for capturing ratio
scale data. For instance, if I were to measure response time (RT) for five different events, I could store
the data in R like this:
> RT 2 * RT
[1] 684 802 1180 782 1108
> RT + 1000
[1] 1342 1401 1590 1391 1554
And to a lesser extent, the “numeric” class is okay for interval scale data, as long as we remember that
multiplication and division aren’t terribly interesting for these sorts of variables. That is, if my IQ score
is 110 and yours is 120, it’s perfectly okay to say that you’re 10 IQ points smarter than me 18 , but it’s
not okay to say that I’m only 92% as smart as you are, because intelligence doesn’t have a natural
zero. 19 We might even be willing to tolerate the use of numeric variables to represent ordinal scale
variables, such as those that you typically get when you ask people to rank order items (e.g., like we do
in Australian elections), though as we will see R actually has a built in tool for representing ordinal data
(see Section 7.11.2) However, when it comes to nominal scale data, it becomes completely unacceptable,
because almost all of the “usual” rules for what you’re allowed to do with numbers don’t apply to nominal
scale data. It is for this reason that R has factors.
18 Taking all the usual caveats that attach to IQ measurement as a given, of course.
19 Or, more precisely, we don’t know how to measure it. Arguably, a rock has zero intelligence. But it doesn’t make sense
to say that the IQ of a rock is 0 in the same way that we can say that the average human has an IQ of 100. And without
knowing what the IQ value is that corresponds to a literal absence of any capacity to think, reason or learn, then we really
can’t multiply or divide IQ scores and expect a meaningful answer.
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