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CHAPTER EIGHT
Two-Way Between-Subjects Design
8.1 COMBINING TWO INDEPENDENT VARIABLES FACTORIALLY
8.1.1 NAMING THE DESIGN
In Chapter 6, we discussed a between-subjects design that contained a
single independent variable (preparation time for the SAT). However,
we are not limited to studying the effects of just one independent vari-
able in a research design. In this chapter, we will deal with the inclu-
sion of a second independent variable (note that we still have only
one dependent variable). A design containing more than one indepen-
dent variable is known as a factorial design when the variables are com-
bined in a manner described in Section 8.1.2. When those independent
variables are between-subjects variables, the design is called a between-
subjects design or a between-subjects factorial design . Designs containing
two between-subjects independent variables that are simultaneously var-
ied are two-way between-subjects (factorial) designs . These designs are
also sometimes referred to as two-way completely randomized designs
because subjects are assumed to be randomly assigned to the various
treatments.
8.1.2 COMBINING INDEPENDENT VARIABLES IN A SINGLE DESIGN
Intertwining two independent variables within the same design is done
by combining them in a factorial fashion in which each level of one
independent variable is combined with each level of the other independent
variable. If our independent variables were, for example, gender (female
and male) and size of city in which participants resided (large and small),
then one combination of the levels of the independent variables might be
females living in large cities. This would then be one condition or group
inthefactorialdesign.Inall,forthisexampletherearefourpossible
combinations and therefore there would be four groups in the study.
Given that in the above example we have four groups in the study, it
istheoreticallypossibletoconceiveofitandtoanalyzeitasafour-group,
one-way design as described in the Chapter 6. So why bother to treat it
as a two-way design and make things more complicated? And if it's so
important to deal with each independent variable as a separate entity, why
not just treat it as two separate one-way designs?
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