experimental design

• Experimental design

  This refers to how we use participants across the conditions of the experiment. There are three types of experimental design, independent groups, repeated measures and matched pairs.
• Independent measures design

  This is a type of experimental design where the participants only complete ONE condition of the experiment.
• Repeated measures design

  This is a type of experimental design where the participants complete all the conditions of the experiment.
• Matched pairs design

  This is a type of experimental design where the participants are recruited and matched and paired up on key participant variables that may affect the DV, for example gender, age and IQ. Each one of the pairs of participants then do one of the conditions of the experiment (one does the control and the other does the experimental condition)
• Order effects

  These are a disadvantage of a repeated measures design and refers to how the positioning of tasks influences the outcome e.g. practice effect or boredom effect on second task. In other words because the participants do both conditions of the experiment, their performance on the second condition may be affected from having done the first condition already (and nothing to do with the IV)
• Practise effect

  This is an example of an order effect that occurs in a repeated measures design when the participant does better on the second experimental condition because they have had practice at doing the task in the first condition, not because of the IV.
• Counterbalancing

  This is a way of dealing with order effects in a repeated measures design. The participants are divided into two groups and one group does condition 1 followed by condition 2, and the other group starts with condition 2 and then does condition 1.
• Participant variables

  This is a disadvantage of an independent measures design. Because we have different participants in the conditions of the experiment, it could be that any differences in the results of the two conditions could be due to the participants in each condition NOT the IV. For example we may have put the most able students at the task in one condition by chance.
• Random allocation

  This is a way of dealing with the problem of participant variables in an independent measures design. Once the participants are recruited for the study they are allocated to the conditions of the experiment using a random method (e.g. names into a hat and draw out one name for condition 1 and another for condition 2, and this is repeated until all participants have been allocated.
• Need more participants

  This is a disadvantage of an independent measures design and a matched pairs design. Because you have different participants in each condition of the experiment you need more participants than if you used a repeated measures design to collect as much data.
• Hard to match participants

  This is a disadvantage of a matched pairs design. Although matching participants removes the issue of participant variables that is a disadvantage of an independent measures design, and also there are no order effects like their would be in a repeated measures design, in reality it is difficult to match participants on all the variables that may be an issue. Its a time consuming design method and difficult to apply.