Psych 5500/6500
Confounding Variables
Fall, 2008
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Confounding Variables
We are running a t test in an attempt to establish a
cause and effect relationship between the
independent and dependent variables. If we reject
H0, we are saying that the difference in the group
means is statistically significant, i.e. that the
difference is larger than would be expected if only
random error were involved. We would then like
to conclude that it was the independent variable
that made the groups means different. We can
only conclude this, however, if we have no
confounding variables in our experiment.
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Confounding (cont.)
A confounding variable is a variable other than our
independent variable (and other than random
chance) that could have caused the group means
to be different.
For example: I.V.=gender, D.V.=respiration rate. We
run the experiment and find that the difference
between the two groups is statistically significant.
We would like to conclude that gender affects
respiration rate. But what if we measured the men
in the morning and the women in the evening, then
‘time of day’ could account for the significant
difference between the groups, ‘time of day’ would
be a ‘confounding variable’.
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Confounding (cont.)
If we have a confounding variable, and we
reject H0, then we do not know whether it
was our independent variable or the
confounding variable that caused the group
means to differ.
If we do not have a serious confounding
variable then when we reject H0 we can
conclude that it was our independent
variable that caused the group means to
differ
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Handling Confounding Variables
Handling confounding variables is an important topic
in experimental design, the important point in a
statistics class is to realize that rejecting H0 only
indicates that the difference between the means
was unlikely if only chance were involved. To then
conclude that the independent variable had an
effect is only reasonable if there are no serious
confounding variables.
Even though this is more a topic for an experimental
design class, I think it would behoove us to take a
quick look at how to handle confounding variables.
We’ll look at four possibilities:
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Handling Confounding
Variables
1) Pretest to see if there is a problem: for
example, before you run your experiment
you could run a quick pretest to see if time
of day influences respiration, if you find it
does not then don’t worry about time of
day being a confounding variable in your
experiment.
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Handling Confounding
Variables
2) Control the variable: if time of day is a
confounding variable then design the
study so that it effects both groups equally,
in this way you are controlling the
confounding variable. For example, run
both groups at the same time, or test one
male and one female at 8:00 in the
morning, one male and one female at 8:30
and so on.
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Control and Treatment Groups
Often the experimenter wants to examine the
effect of some manipulation (treatment). To
do so two groups are created, the ‘control’
group that does not get the treatment and
the ‘treatment’ group that does. The goal is
to control all of the possible serious
confounding variables, so that the only
difference between the control group and the
treatment group is the treatment itself.
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Handling Confounding Variables
3) Randomize the variable: sometimes you may
not be able to control the confounding variable.
If this is the case, then arrange for its effect to be
randomly divided among the participants. For
example, randomly determine what time of day
each participant is measured. The confounding
variable might still effect the data (e.g. by luck
more males may be measured in the morning
than females) but at least its effect will be
random, and will have an equal chance of
contradicting your theory’s prediction as
supporting it. Randomly assigning subjects to
groups randomizes the effect of individual
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differences.
Handling Confounding
Variables
4) Include the confounding variable in your
model: there are a lot of advantages to
looking at how both time of day and
gender affect respiration (rather than trying
to get rid of time of day so you can just
look at gender). We will be covering this
next semester when we get to the ‘Model
Comparison Approach’.
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Experimental Designs
The t test for independent groups can be used
in three different types of experimental
designs. Some of these designs introduce
additional confounding variables.
1. True Experimental Design
2. Quasi-Experimental Design
3. Static Group Design
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True Experimental Design
Attributes:
1. Subjects are randomly divided into groups.
2. The independent variable is manipulated by
the experimenter
If performed correctly, then the only reason the
groups should differ before the IV is applied is
because of random chance. This design may
still have confounding variables (introduced by
the how you manipulate the independent
variable), but it tends to have fewer confounding
variables than the other two designs.
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Quasi-Experimental Design
Attributes:
1. Subjects are not randomly divided into groups, they
are in pre-existing groups that you hope are essentially
the same (i.e. as if you had randomly divided into
groups).
2. Independent variable manipulated by the
experimenter.
This design is used when you would like to do a true
experimental design but practical reasons make that
impossible. Additional confounding variables are
introduced by possible pre-existing, non-random,
differences between the groups. Possible solutions:
search for groups that are equal, test for possible
confounding variables.
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Quasi-Experimental Design (cont.)
For more information on quasi-experimental designs you may
want to read the influential work of Campbell. You could
start with:
Campbell, Donald T. (1969). Reforms as experiments. American
Psychologist, 24, 409-429.
In looking for what he has done recently I ran across the
following (which I haven’t read) but looks good for light
summer reading (seriously, looks good if you really want
to get into the topic of establishing cause and effect with
experiments).
Shadish, W. R., Cook, T. D., Campbell, D. T. (2002) Experimental and
quasi-experimental designs for generalized causal inference.
Boston: Houghton Mifflin and Company. 623 pp.
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Static Group Design
Attributes:
1. Subjects are not randomly divided into groups, the
groups are pre-existing.
2. The independent variable is not manipulated by the
experimenter, instead it is the variable you use to
define the two groups.
In this design you are explicitly looking for pre-existing
differences between the groups (which is your I.V.). In
the gender example the I.V. (gender) was what you used
to divide participants into the two groups. Additional
confounding variables are pre-existing differences other
than your I.V. (i.e. other variables that systematically
differ along with the I.V., such as height differences that
occur when you divide participants based upon gender).15
Static Group Design (cont.)
The static group design is considered to be a
correlational design as the independent variable
is not manipulated. It shows up here, however,
rather than in the latter lecture on correlation as
the focus here is on comparing the two group
means, rather than upon a correlation between
two variables.
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