Dissertations and research projects

True experiments seek to establish cause and effect relationships between a group of variables. Researchers control for all variables except for the variable(s) being manipulated, to establish its effect on the dependent variable.

Between-subjects designs involve the assignments of participants to one of two (or more) conditions, with each participant experiencing only that condition. In its simplest form, a between-subjects design requires a control condition and a treatment condition. If the results of an experiment differ greatly between conditions, then it can be assumed that this due to the effect of the intervention or manipulation that has been applied in the treatment condition. To help minimise the affect of extraneous variables that might impact differences between the groups (and increase the likelihood that observed differences are due to the effect of the independent variable), participants in the control and treatment conditions might be matched for relevant characteristics.

For example, in an experiment to assess the effectiveness of two training programmes in improving athletic performance, participants might be matched for some key measures of fitness such as 100m sprint time, maximum squat etc. This would enable researchers to be more confident that any changes to athletic performance in the participants between the two groups were likely due to the training programme they undertook, rather than natural, pre-exiting differences in athletic performance.

Sometimes referred to as repeated measures, this approach involves obtaining more than one measure from each participant in a study. This means that participants take part in both the control and treatment condition(s). The primary advantage of this is that participants act as their own control; you can be more confident that any observed differences result from the treatment condition rather than naturally occurring differences between the groups. One problem, however, is that of order effects (sometimes called practice effects). These effects may occur because conditions are applied one after the other and this can lead to changes in performance that are not the result of the treatment but instead reflect some effect of the previous condition that a participant has experienced. For example, improvements in performance could be due to learning/practise and a decline in performance could be due to fatigue over experiencing two (or more) experimental conditions back-to-back.

One way to account for this problem is to counterbalance the order of your conditions. To do this, a researcher would ensure that each condition in the experiment is experienced 1st for an equal number of participants:

10 participants experience condition A 1st and condition B 2nd
10 participants experience condition B 1st and condition A 2nd

Doing this helps to reduce the impact of order effects by ensuring that any effects are distributed evenly across all conditions.

Another option could be to create a long time between testing conditions to reduce any possible effects of learning and/or fatigue. It should be noted however, that creating distance between conditions isn’t always practical and it can be hard to know how long is sufficient to eliminate a potential order effect: this is particularly true for practice effects as it can be hard to accurately determine how long it takes for potential improvements in performance due to learning, to disappear.

The final type of design is used when a research design has one (or more) factors that is between subjects and one (or more) factors that is within subject. This is often used when for research that is looking at the effect on an intervention in relation to another factor that has a fixed effect.

For example, if a researcher was looking at the effectiveness of a drug for treating pain in those under 40 and over 40, age would be a between-subjects factor because a participant can’t be both under and over 40 at the same time. The drug that participants take would be the within-subjects factor. This type of design is particularly useful if you want to examine if the effect on an intervention is different dependent upon another factor. In the example above, it would be possible to establish if the effect of the drug was beneficial for all participants, or whether it was particularly effective/ineffective depending on the age of the participant – whether they were under or over 40. 

These are similar to true experiments: the aim is to establish cause and effect relationships. Crucially however, assignment to groups is not random. This type of design is often used when it is not possible for the researcher to randomly assign participants to groups because they are interested in understanding a particular phenomenon in relation to naturally occurring differences between groups – an example of this could be an experiment where a researcher is interested in examining whether the effect of coffee consumption on sleep differs depending on age. In this example, it is impossible for the researcher to manipulate the age of participants, so instead group assignment would be made based on predetermined criteria e.g. under 40, 40+. As this assignment cannot be random this would be a quasi-experiment.