Page 38 - Statistics II for Dummies

22 Part I: Tackling Data Analysis and Model-Building Basics

Categorical versus Quantitative

Variables

After you’ve collected all the data you need from your sample, you want to
organize it, summarize it, and analyze it. Before plunging right into all the
number crunching though, you need to first identify the type of data you’re
dealing with. The type of data you have points you to the proper types of
graphs, statistics, and analyses you’re able to use.
Before I begin, here’s an important piece of jargon: Statisticians call any
quantity or characteristic you measure on an individual a variable; the data
collected on a variable is expected to vary from person to person (hence the
creative name).
The two major types of variables are the following:

✓ Categorical: A categorical variable, also known as a qualitative variable,
classifies the individual based on categories. For example, political
affiliation may be classified into four categories: Democrat, Republican,
Independent, and Other; gender as a variable takes on two possible cat-
egories: male and female. Categorical variables can take on numerical
values only as placeholders.
✓ Quantitative: A quantitative variable measures or counts a quantifiable
characteristic, such as height, weight, number of children you have,
your GPA in college, or the number of hours of sleep you got last night.
The quantitative variable value represents a quantity (count) or a mea-
surement and has numerical meaning. That is, you can add, subtract,
multiply, or divide the values of a quantitative variable, and the results
make sense as numbers.

Because the two types of variables represent such different types of data,
it makes sense that each type has its own set of statistics. Categorical vari-
ables, such as gender, are somewhat limited in terms of the statistics that
can be performed on them.

For example, suppose you have a sample of 500 classmates classified by
gender — 180 are male and 320 are female. How can you summarize this
information? You already have the total number in each category (this sta-
tistic is called the frequency). You’re off to a good start, but frequencies are
hard to interpret because you find yourself trying to compare them to a total
in your mind in order to get a proper comparison. For example, in this case
you may be thinking, “One hundred and eighty males out of what? Let’s see,
it’s out of 500. Hmmm . . . what percentage is that?”

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