Experimental Designs

Experimental Design Definition

To investigate the effect of our independent variable on the dependent variable, we need to expose participants to experimental conditions. The independent variable is then manipulated between these conditions.

The question now should the same group be subjected to both levels of your independent variable? Or should you create two groups where each is assigned to only one condition? These are questions related to choosing your experimental design.

Types of Experimental Design

There are four types of experimental design: independent measures, repeated measures, matched pairs, and quasi-experimental designs.

Various factors determine the type of experimental design used, like:

• The research method used.
• Whether the researcher can manipulate the variables studied.
• Whether participants can be randomly allocated into the experimental (or control) groups.
• Characteristics of participants.

Let's now go through each experimental research design used in psychology.

Experimental Research Design: Independent Measures

The independent measures design is an experimental design in which you only assign participants to one of your experimental conditions. This experimental design is also known as the between-subjects design.

However, if you're comparing two different groups of people, you need to account for the potential individual differences between the groups to ensure your findings are valid. Randomly assigning participants to groups is one way to average out any between-group differences.

Random assignment means that each participant has a 50% chance of being assigned to either group. When you have two conditions, this could be done by flipping a coin when assigning each participant to the group or using a random number generator.

Fig. 2 - Hypothetical results from the experiment investigating the impact of sleep on exam performance.

Using the independent measures design, you can compare the two experimental groups in your experiment. This design also allows you to avoid order effects and demand characteristics. Order effects occur when the order in which participants participate in the condition affects their performance.

Demand characteristics occur when participants guess the aim of your experiment and adjust their behaviour to what they think is expected.

Experimental Research Design: Repeated Measures

In a repeated measures design, participants take part and are assessed in each experimental condition. Therefore, the data for each IV condition come from the same participants. This experimental design is also referred to as a within-subjects design.

This approach eliminates the potential individual differences between groups, which is a confounding variable. This way, repeated measures design increases the study's validity.

It also makes recruitment easier, as we need half the number of participants that we would have needed for the independent measures design. However, this design introduces the risk of order effects as well as demand characteristics.

Moreover, if the participants know what conditions you're investigating, they can modify their behaviour to fit what they think you expect.

To minimise order effects in repeated-measures experimental designs, we can counterbalance the order in which participants participate in the two conditions. Counterbalancing involves subjecting half of the participants to the first condition first and the other half to the second condition first. This way, it is possible to determine how order effects influenced the results.

Experimental Research Design: Matched-Pairs

Similarly to the independent-measures design, the matched-pairs design involves subjecting participants to only one experimental condition. However, in this design, the assignment process is more complex. Participants are first paired based on specific characteristics that could be potential confounding variables. Then each individual in the matched pair is randomly assigned to an experimental or control group.

In some way, this design combines the best of both words – it allows us to minimise both the order effects and the individual differences. However, this design can be more complex, costly and time-consuming.

Finding two groups of participants that match all the key characteristics that might influence your results can be difficult.

Quasi-experimental Research Design

The quasi-experimental design is similar to the independent measures design, as it tests different participants at each independent variable level, except that participants are not randomly assigned to conditions. Instead, this design utilises naturally existing groups and investigates differences between them.

The advantage of this design is that it has high external validity, as it involves testing participants in real-life settings. But it can introduce individual differences that could confound our results.

Fig. 3 - Some variables like demographics, genes or personality cannot be manipulated. When investigating these variables, we need to rely on quasi-experimental designs.

Experimental Design Examples

Independent measures research example:

• Investigating how the amount of sleep influences attention by recruiting 20 participants and randomly assigning half to each experimental condition (4 hours and 10 hours of sleep).

Repeated measures research example:

• Investigating differences in depression scores before and after cognitive behavioural therapy treatment.

Matched-pairs research example:

• Investigating the effect of exercise on weight loss, 50 participants were recruited. The participants were paired by BMI, age and gender, two males aged 21 with a BMI of 28, two females aged 22 with a BMI of 26, etc. These pairs were randomly assigned to the control group (no exercise) and the experimental group (2h of moderate-intensity exercise a week) to measure weight loss.

Quasi-experimental research example:

• Investigating the effectiveness of CBT for participants with depression or schizophrenia diagnosis. Here, the naturally occurring independent variable is the psychiatric diagnosis.

Evaluation of Experimental Design Psychology

Let's compare the occurrence of potential confounding variables (order effects, demand characteristics and individual differences) in each experimental design type.