If you have decided that an experiment is the best approach to testing your hypothesis, then you need to design the experiment.
Experimental design refers to how participants are allocated to the different conditions (or IV groups) in an experiment.
Probably the commonest way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group and not the control group.
The researcher must decide how he/she will allocate their sample to these IVs. For example, if there are 10 participants, will all 10 participants take part in both conditions (e.g. repeated measures) or will the participants be split in half and take part in only one condition each?
1. Independent Measures:
Different participants are used in each condition of the independent variable. This means that each condition of the experiment includes a different group of participants. This should be done by random allocation, which ensures that each participant has an equal chance of being assigned to one group or the other.
Independent measures involves using two separate groups of participants; one in each condition. For example:
- Pro: Avoids order effects (such as practice or fatigue) as people participate in one condition only. If a person is involved in several conditions they may become bored, tired and fed up by the time they come to the second condition, or becoming wise to the requirements of the experiment!
Con: More people are needed than with the repeated measures design (i.e. more time consuming).
Con: Differences between participants in the groups may affect results, for example; variations in age, sex or social background. These differences are known as participant variables (i.e. a type of extraneous variable).
2. Repeated Measures:
The same participants take part in each condition of the independent variable. This means that each condition of the experiment includes the same group of participants.
- Pro: Fewer people are needed as they take part in all conditions (i.e. saves time)
Con: There may be order effects. Order effects refer to the order of the conditions having an effect on the participants’ behavior. Performance in the second condition may be better because the participants know what to do (i.e. practice effect). Or their performance might be worse in the second condition because they are tired (i.e. fatigue effect).
Suppose we used a repeated measures design in which all of the participants first learned words in loud noise and then learned it in no noise. We would expect the participants to show better learning in no noise simply because of order effects.
To combat order affects the research counter balances the order of the conditions for the participants. Alternating the order in which participants perform in different conditions of an experiment.
The sample is split into two groups experimental (A) and control (B). For example, group 1 does ‘A’ then ‘B’, group 2 does ‘B’ then ‘A’ this is to eliminate order effects. Although order effects occur for each participant, because they occur equally in both groups, they balance each other out in the results.
3. Matched Pairs:
One pair must be randomly assigned to the experimental group and the other to the control group.
Pro: Reduces participant (i.e. extraneous) variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
- Pro: Avoids order effects, and so counterbalancing is not necessary.
Con: Very time-consuming trying to find closely matched pairs.
Con: Impossible to match people exactly, unless identical twins!
Experimental Design Summary
Experimental design refers to how participants are allocated to the different conditions (or IV groups) in an experiment. There are three types:
1. Independent measures / groups: Different participants are used in each condition of the independent variable.
2. Repeated measures: The same participants take part in each condition of the independent variable.
3. Matched pairs: Each condition uses different participants, but they are matched in terms of certain characteristics, e.g. sex, age, intelligence etc.