This is also referred to as the related samples design or the within groups design. In this design the participant performs in both the experimental condition and the control condition.
Each participant, therefore, provides us with two scores or paired data. It’s then a case of comparing all the paired data scores to see if there’s a difference between the conditions.
Now the main strength of this design over the independent subjects design is that you don’t have to worry about individual differences confounding the results of your experiment because if you think about it, each participant acts as their own control. In other words they take their random variability i.e. number of hours sleep, personality etc. with them across the conditions so that they cancel each other out.
The danger of this type of design is that Order Effects will confound your results. Because each participant is being asked to do something twice it’s possible that they will perform better in the second condition because of practice or that they perform worse in the second condition because of fatigue or boredom.
Therefore, we must ensure that the order of the two conditions is counterbalanced.Counterbalancing is a technique employed to ensure that half the subjects perform in the experimental condition first and the control group second. And half the subjects perform in the control group first and the experimental group second.
Counterbalancing does not get rid of order effects but it does makes sure that any possible confounding effects cancel each other out.Independent subjects and repeated measures are the two most common types of design in experimentation and the thing to note about them is that their strength lies in the weakness of the other.
With independent subjects design you don’t have to worry about order effects because you aren’t getting people to perform twice; but in the repeated measures design you are this is can be a real problem. However, in the independent subjects design you have to worry about individual differences between conditions; but in the repeated measures design you don’t because people take their differences with them, thereby nullifying any possible effect.