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

by OC12601

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**preview**shows pages 1-2. to view the full**7 pages of the document.**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.

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

groups

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

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.

•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.

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