Independent Group Design

What are Independent Groups Designs?

An independent groups design is an experimental design where different participants are used in each condition in an experiment. Participants are randomly allocated.

Fig. 1 - Independent group designs use different participants in each condition.

Independent Groups Design Definition

Independent group design (IGD) is an experimental design whereby different participants are used for each experimental condition. The researcher exposes separate sets of randomly allocated participants to different experimental conditions.

A hypothetical study compared the results to see how one group (the experimental sleep-deprived group) performed compared to another (the control, well-rested group) in a memory test.

In this experimental design, different participants participated in different conditions, i.e. the sleep-deprived versus well-rested group. And this characteristic is what makes this study an independent group design. The defining characteristic of this experimental design is that each group uses different participants.

Experimental Design: Independent Groups

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 the necessary measures, the results may be considered valid and reliable.

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.

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.

Fig. 2 - Scientific research involves assigning participants to experimental conditions; an example is the independent group design.

Independent Group Design: Psychology Research Examples

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 that explored how different amounts of sleep affect people's reaction times. The hypothetical study recruited two groups of ten different individuals.

Whilst one group had ten hours of sleep per night, the other group had three; this means that the independent variable will be the amount of sleep (the manipulated variable).

The dependent variable (the measured variable) was their recorded reaction time.

One group had ten hours of sleep, whilst the other had three, and different participants were tested in each condition, so the experimental design was an independent group design.

A second theoretical study used an independent group design to test the potential side effects of a prescription drug. Participants were split into two groups, group A and group B.

Group A was given the drug, and group B was given a 'placebo' drug (one with no active ingredients). From this research, we would expect no side effects in Group B as no active ingredients mean that the medication will not affect them.

In this case, the independent variable is the drug, and the dependent variable is the possible side effects of the drug.

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. This is one of the many benefits of using this experimental design compared to others.

Advantages of Independent Groups Design

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 because it may not be possible to test different participants in each condition.

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.

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 simultaneously.

Limitations of Independent Groups Design

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 - the changes in the DV observed may be because of participant variables.

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 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.