Experimental Designs

Independent Measures Design

Different participants are used in each condition of the independent variable.
This helps to prevent order effects such as practise or fatigue influencing the results.
However, it introduces participant variables, differences between participants that may affect the results (e.g., intelligence, mood, age).

The same participants are used in all the conditions of the independent variable.
This helps control participant variables as each participant is their own control.
But it might introduce order effects, such as boredom or improved performance due to practise.

Different, but similar participants are used in each condition of the independent variable. They are deliberately matched on key characteristics.
This attempts to control both participant variables and order effects.

Random allocation is used to assign participants to conditions in the independent measures and matched pairs design. This helps to avoid systemic bias and ensure the experiment is fair.

In repeated measures design, counterbalancing is used to control for order effects. This involves changing the order in which tests are administered.

Standardisation is used to ensure that every participant is tested in the same way. This increases reliability and makes replication of the study easier.

Independent measures is suitable when order effects are likely to heavily influence performance.
Repeated measures is useful when the researcher has a small sample size or wants to reduce participant variables.
Matched pairs is best when both participant variables and order effects may heavily impact results, but it requires significant time and resources for participant matching.
The choice of design should be based on the nature of the experiment and its practical constraints.