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Chapter 8

PSYB01 Textbook Notes - Chapter 8

8 Pages
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Department
Psychology
Course Code
PSYB01H3
Professor
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
randomization
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 randomization.
There are two types of basic experimentsPosttest-only Design and Pretest-
Posttest Design
Posttest-Only Design (diagram pg 151)
Researcher must:
1. obtain two equivalent groups of participantsto 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
participants.
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2. introduce the independent variablethe 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
amounts of the independent variable (eg. Effect of the amount of relaxation
training on quitting smoking). Both methods provide a basis comparing the two
groups
3. measure the effect on the dependant variablethe 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 experiment.
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 manipulation.
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.
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One disadvantage of a pretest is they may be time consuming and awkward to
administer.
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.
To overcome awareness of the pretest, it can be disguised by administering it in a
completely different setting by a different experimenter.
A second measure of disguise is to embed the pretest in a set of irrelevant
measures so it is not obvious to the participant what the research topic is
Solomon Four-Group Design - It is possible to test the impact of the pretest with a
combination of the posttest-only and the pretest-posttest designs. Half the
participants receive only the posttest and half the participants receive both the
pretest and the posttest. If there is no impact of the pretest, the posttest scores will
be the same in both control groups and in both experimental groups.
Look at table 8.1 on page 154 and the graphs on page 155
Assigning Participants to Experimental Controls
There are two basic ways of assigning participants to experimental conditions.
Independent groups designparticipants are randomly assigned to the various
conditions so that each participates in one group only
Repeated measures designparticipants are in all conditions. The participants
are measured after receiving each level of the independent variable.
Independent Groups Design
Participants are assigned to each of the conditions using random assignment.
In practice, researchers usually use a sequence of random numbers to determine
assignment (Appendix C). the table is made up of a list of numbers from 0 – 99
that were randomly assigned by a computer
Randomization eliminates any systematic biased and groups will be equivalent
(eg on age, race, income, education etc) therefore participant differences can not
be an explanation for the results of the experiment.
Repeated Measure Design
The same individuals participate in all of the groups.
Eg. In an experiment investigating the relationship between the meaningfulness of
information and learning of that information, the same individuals might first read
low-meaningful material and then take a memory recall test to measure their
learning, then those same individuals may read high-meaningful material and take
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Description
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 randomization 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 randomization. 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 participants. www.notesolution.com
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