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Chapter 2: Sorting through Statistical Techniques
In 2003, the Pew Research Foundation studied the following variables in terms
of their relationship with political affiliation: gender, race, state of residence,
income level, age, education, religion, marital status, and whether or not you
have children. While you can do individual Chi-square analyses to examine
possible connections between each of these variables and political affiliation
separately, you can’t find out which combinations of these variables increase
the likelihood of someone being a Democrat, Republican, or other.
For example, the Foundation found that women are more likely to be
Democrats than men, but age is also a factor. Younger people tend to be more
inclined to be Republican, and older people lean toward being Democrat.
However, if you look at the combination of gender and age, you can see
mixed results; males who are older are more likely than young females to be
Democrat rather than Republican, for example. This kind of result is called an
interaction effect between gender and age group. An interaction effect occurs
when certain combinations of variables produce different results than other
combinations. The only way to look for these kinds of more-complex relation-
ships is to do model building, which allows you to examine the combinations 37
of variables and their impact on political affiliation. The Pew Foundation was
able to make conclusions about the United States population based on its
model linking political affiliation, age and gender, as well as their interactions.
Statistics for Quantitative Variables
Quantitative variables, unlike qualitative variables, have a wider range of statis-
tics that you can do, depending on what questions you want to ask. The main
reason for this wider range is that quantitative data are numbers that represent
measurements or counts, so it makes sense that you can order, add or sub-
tract, and multiply or divide them — and the results all have numerical mean-
ing. Examining quantitative date opens up a whole world of possibilities for
analysis. In this section, I present the major data-analysis techniques for quan-
titative data. I further expand each technique in later chapters of this book.
Making comparisons
Suppose you want to look at income (a quantitative variable) and how it
relates to a qualitative variable, such as gender or region of the country. Your
first question may be: Do males still make more money than females? In this
case, you can compare the mean incomes of two populations — males and