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The phrase 'practise makes perfect' rings true for many areas of life, including research and experiments. Humans, by nature, get better at things by experiencing and learning them repeatedly. However, doing the same tasks over and over again can cause problems when analysing test effects. The participants might get used to this type of test after doing it a few times. Also, repeating the same thing can make them feel bored and tired.
A way to avoid these issues is by using an independent groups design (IGD).
Independent group design (IGD) is an experimental design whereby different participants are used for each experimental condition. The researcher exposes separate sets of participants to different experimental conditions. Each group has different participants.
Group of people, freepik.com/pch.vector
An experimental design is a research method that aims to test a hypothesis and ensure that research is executed in a controlled and scientific manner. This leads to collecting representative data and drawing conclusions. If the researchers follow the appropriate steps and implement necessary measures, the results are most likely valid and reliable.
However, research constantly undergoes evaluations, and future studies may find gaps in older ones.
In an independent group design, think of an experimental and a controlled group. For the experimental group of individuals, there is a manipulation of the independent variable.
To remember these terms, you can think of them as ‘cause’ and ‘effect’ values.
The independent variable acts as the ‘cause’ factor in the experiment. It is the manipulated variable that is independent of other variables.
The value of the dependent variable depends on the changes of the independent variable. It is the ‘effect’ factor. It is the variable being measured.
Independent group design differs from a repeated group design, which uses the same individuals on two or more different occasions for different experimental conditions. In repeated measures designs, all participants do each task.
Scientist researching, freepik.com/pch.vector
Several study examples present what makes up an independent group design. In the following text, we will present two.
Let's consider a study on sleep. If you want to find out how different amounts of sleep affect people's reaction times, you can have two groups of ten different individuals.
Whilst one group will have ten hours of sleep per night, the other group will have three. This means that the independent variable will be the amount of sleep (which is manipulated).
The dependent variable will be their reaction time.
| The hypothesis | less sleep causes slower reaction times |
| The independent variable | the amount of sleep groups 1 and 2 can have |
| The dependent variable | the reaction time of individuals in groups 1 and 2 |
One group has ten hours of sleep, whilst the other has three. There are different participants in each group, so it is an independent groups design.
An independent group design can also be used to test the potential side effects of a prescription drug. Divide a group of participants into two, calling them group A and group B.
If group A is given the drug, and group B is given a 'placebo' drug (one with no effects), it should be obvious to see if the drug being tested has side effects.
In this case, the independent variable is the drug, and the dependent variable is the possible side effects of the drug.
| The hypothesis | drug X causes side effect Y |
| The independent variable | the drug taken by the individuals |
| The dependent variable | the potential side effect |
Group A is testing the drug and will not take the placebo. Group B is not testing the drug and will take the placebo. Each group experiences different testing conditions, so we can attribute differences in the results to the specific manipulation of the independent variable, that is, taking and not taking the drug.
There are both advantages and disadvantages to using an independent group design to test a hypothesis.
However, sometimes researchers must use an independent group design. This is because the independent variable may not allow individuals to belong to both groups (for instance, if it is a male and female split).
A key advantage to independent group designs is that there are no order effects. Since different participants are exposed to each condition, the order in which they are done does not affect the outcome. Participants are more likely to act accordingly. They cannot practice and get better. They also will not become fatigued or bored.
Another advantage is that, since more participants are required because each can only belong to one group, the results have increased external validity.
External validity refers to the extent to which findings of a study can be generalised to other people, situations, and environments.
The more people involved, the more likely the differing representation and thus the higher the external validity.
Finally, there is always the benefit of both time, effort, and money saved because both groups of participants can be tested at the same time.
Because there are two groups of different individuals, more participants are needed. This may outweigh the benefit of testing both groups because, for instance, more equipment may be required for more individuals, making independent group design less economical.
Participant variables exist. Since there are different groups of individuals, it is difficult to ensure that the differing results are because of the manipulated independent variable - they may be because of participant variables.
When considering the first example above, which shows the effect of reaction time on sleep, the general prediction is that those in the group limited to three hours of sleep have slower reaction times.
But what if over half of these individuals had personal, participant-specific conditions that slowed their reaction time regardless of how many hours they'd slept? For instance, poor diets, or less dexterity/ability to react overall as they have never experienced the conditions before, whereas someone who plays sports or videogames may have better reaction times, affects the recording of the results.
This is known as a participant variable.
Another example may be that participants in one condition may be more intelligent (with higher IQs) than others.
Some variables are not always confounding because they do not matter as much (it depends on whether or not they are relevant to the outcome). But, if they do matter, this may lead to invalid conclusive data.
So, how can we lower the chance of participant variables?
The answer is random sampling. Although this does not entirely eliminate the issue, it reduces the chances of participant variables confounding the experiment results.
Random sampling means that each member of a population has an equal chance of being selected through random list generators on computers or placing names in a container and drawing them out randomly.
Random sampling is advantageous for independent group design experiments because of the unbiased selection. This increases the chance of having a more representative sample of participants, thus increasing the results' external validity.
Independent group design (IGD) is a type of experimental design whereby different participants are used in each experimental condition. The researcher creates an 'experimental' and a 'controlled' group. For the experimental group, the independent variable is manipulated.
An example of independent groups design is testing the hypothesis that less sleep causes slower reaction times. The researcher can divide the participants into two groups to test this hypothesis – one group getting ten and the other three hours of sleep.
The independent variable is the amount of sleep, and the dependent variable is the reaction time.
You can overcome the disadvantages by using random sampling. This introduces an unbiased selection process which increases the likelihood of having a representative sample and increases the external validity of the results.
Another disadvantage is the costs due to many participants. One way to overcome this problem is to be cost-efficient with the equipment needed.
A quasi-experiment design differs from an independent group design because the former does not depend on random sampling or assignment of individuals. In quasi-experiment, subjects are specifically assigned to their groups based on selective criteria.
The 3 different types of independent group designs are random group designs, matched group designs, and natural group designs.
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