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
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
•Differs from the “Posttest-Only Design” because it gives a pretest before the
experimental manipulation to ensure that the groups were actually equivalent
•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.