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PSYB01H3 Study Guide - Repeated Measures Design, Random Assignment, Dependent And Independent Variables

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Anna Nagy

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Chapter 8 – Experimental Design
In the experimental method, all extraneous variables are controlled
Confounding and Internal Validity
The experimental method provides an unambiguous interpretation of results
because the independent variable is manipulated by the researcher to create groups
that differ in the levels of the variable, which are then compared in terms of their
scores of the dependant variable
All other variables are kept constant, either through experimental control or
If the scores of the groups are different, can conclude that it was caused by the
independent variable (because that was the only difference between the groups).
A confounding variable is a variable that varies along with the independent
variable – confounding occurs when the effects of the independent variable and the
uncontrolled variable are intertwined so you cannot determine which variable
caused the observed effect.
Good experimental design eliminates possible confounding that results in possible
alternative explanations, because only by eliminating competing, alternative
explanations can we draw a causal relationship from the independent variable.
Internal Validity – when the results of an experiment can confidently be attributed
to the independent variable (and not any alternate explanations).
Basic Experiments
The simplest experimental design has two variables – the independent and
dependant variables
The independent has two levels – the control group and the experimental group
Researchers make every effort to ensure the only difference between groups is the
manipulated variable – remember experiments involve control over extraneous
variable through keeping such variables constant (control group) or by
There are two types of basic experiments – Posttest-only Design and Pretest-
Posttest Design
Posttest-Only Design (diagram pg 151)
Researcher must:
1. obtain two equivalent groups of participants – to eliminate and potential
selection differences: the people selected to be in the conditions cannot differ in
any systematic way. The groups can be made equivalent by randomly assigning
2. introduce the independent variable – the researcher must choose 2 levels of the
independent variable, such as the experimental which receives a treatment and the
control which does not. The researcher could also choose to use two different

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amounts of the independent variable (eg. Effect of the amount of relaxation
training on quitting smoking). Both methods provide a basis comparing the two
3. measure the effect on the dependant variable – the measurement procedure is
kept the same for both groups so that comparison is possible. While a statistics test
would be conducted, for our purposes, know that this produces an internally valid
Pretest-Posttest Design
Differs from the “Posttest-Only Design” because it gives a pretest before the
experimental manipulation to ensure that the groups were actually equivalent before
This is usually not necessary if the participants were randomly assigned. Larger
sample sizes produce groups that are virtually identical in every aspect.
the larger the sample, the less likelihood that the groups will be systematically
different and the more likely that the effect viewed in the dependant variable is due
to the independent variable.
Rule of thumb: at least 20-30 participants per group.
Advantages and Disadvantages of the Two Designs
While randomization is likely to produce equivalent groups, when there are smaller
samples it is possible that they are not equal, so a pretest allows the researcher to
assess whether the groups are in fact equivalent.
Sometimes a pretest is necessary to select the participants of the experiment. For
example, it may be used to locate the highest and lowest scores on a smoking
measure. Once identified, the participants will be randomly assigned to the
experimental and control groups.
The pretest can also be used to the extent of change in each individual (eg compare
the smoking measure before and after the treatment).
A pretest is necessary whenever there is a possibility that the participants will drop
out of the experiment (eg studies over a long period of time). The dropout factor in
experiments is called mortality.
Even if the groups are equivalent to begin with, different mortality rates will effect
the results greatly. For example, if the heaviest smoker from one group drops out,
and only the lighter smokers are left, the treatment will seem more effective than it
actually is. In this way, mortality can become an alternate explanation for the
effects seen.
A pretest allows you to asses the effects of mortality – you can look at the pretest
scores of the dropouts and know whether mortality affected the final results.
One disadvantage of a pretest is they may be time consuming and awkward to
The most important disadvantage of a pretest is that it may sensitize participants
and allow them to figure out your hypothesis, therefore changing the way they react
to the manipulation – therefore, the independent variable may not have an effect in
the real world, where pretests are not given.
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