Repeated Measures Design

Where the same participants are allocated to all groups (i.e. take part in all conditions) of an experiment.

Strengths

The results will not be subject to participant variables (i.e. individual differences between participants), putting more confidence in dependent variable changes being solely due to manipulated changes in the independent variable.

As the same participants are used [at least] twice, extra participants do not need to be recruited.

Weaknesses

There is risk of observing order effects (e.g. practice / fatigue effects, or demand characteristics), but this risk be reduced by counterbalancing (i.e. controlling the order of variables so that each order combination occurs the same number of times, e.g. one half of participants partake in condition A followed by B, whereas the other half partake in B followed by A).

If a participant drops out, data will be lost from all conditions of the experiment rather than one.

Independent Groups Design

Where different participants take part in each experimental condition (they will be allocated randomly).

Strengths

Order effects cannot be observed, as no participants will be used in more than one condition.

Data collection will be less time-consuming if all conditions of the experiment can be conducted simultaneously.

Weaknesses

Different participants need to be recruited for each condition, which can be difficult and expensive.

There is a risk of participant variables (individual differences between participants) affecting the results between conditions, rather than solely manipulation of the independent variable.

Matched Pairs Design

Where participants take part in only one experimental condition, but they are recruited specifically to be similar in relevant characteristics (e.g. intelligence, gender, age) to ‘matched’ participants in the other condition(s).

Strengths

Order effects will not be observed as participants only take part in one condition.

The tailored participant-matching process reduces the risk of participant variables (individual differences) from affecting results between conditions.

Weaknesses

Different participants need to be recruited for each condition, which is difficult and expensive.

Matching is a more complex process, and it will always be very difficult to match participants identically.