Page 53 - Intermediate Statistics for Dummies

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Part I: Data Analysis and Model-Building Basics
Qualitative versus Quantitative Variables
in Statistical Analysis
After you’ve collected all the data you need from your sample, you want to
organize it, summarize it, and analyze it. Before plunging the data in to do
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 quan-
tity or characteristic you measure on an individual a variable; the data col-
lected on a variable is expected to vary from person to person (hence the
creative name).
The two major types of variables are the following:
Qualitative: A qualitative variable classifies the individual based on cat-
egories. For example, political affiliation may be classified into four cate-
gories: Democrat, Republican, Independent, and other; gender as a
variable takes on two possible categories: male and female. A person
may be categorized as a female Republican, which means that, regarding
the gender variable, she falls into the female category, and regarding the
political affiliation variable, she falls into the Republican category.
Another name for a qualitative variable is a categorical variable.
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. This characteristic isn’t true of qualitative vari-
ables, which can take on numerical values only as placeholders.
Because the two types of variables represent such different types of data, it
makes sense that each type has its own set of statistics. Qualitative variables,
such as gender, are somewhat limited in terms of the statistics that can be per-
formed on them. For example, suppose you have a sample of 500 classmates
classified by gender — 180 of them are male, and 320 are female. How can you
summarize this information? You already have the total number in each cate-
gory (this statistic is called the frequency). You’re off to a good start, but fre-
quencies 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. In the previ-
ous example, 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? I can’t think.”