Experimental designs that study two or more independent variables at the same time are called

In experiments, you test the effect of an independent variable by creating conditions where different treatments (e.g. a placebo pill vs a new medication) are applied.

In a between-subjects design, or a between-groups design, every participant experiences only one condition, and you compare group differences between participants in various conditions. It’s the opposite of a within-subjects design, where every participant experiences every condition.

A between-subjects design is also called an independent measures or independent-groups design because researchers compare unrelated measurements taken from separate groups.

Using a between-subjects design

In a between-subjects design, there is usually at least one control group and one experimental group, or multiple groups that differ on a variable (e.g., gender, ethnicity, test score etc.)

Every experimental group is given an independent variable treatment that the researcher believes will have some effect on the outcomes, while control groups are given no treatment, a standard unrelated treatment, or a fake treatment.

You compare the dependent variable measures between groups to see whether the independent variable manipulation is effective. If the groups differ significantly, you can conclude that your independent variable manipulation likely caused the differences.

You use a between-subjects design to divide the sample into two groups:

• A control group where the participants see the current business slogan on the website,
• An experimental group where the participants see the new slogan on the website.

Then, you compare the percentage of newsletter sign-ups between the two groups using statistical analysis.

Ideally, your participants should be randomly assigned to one of the groups to ensure that the baseline participant characteristics are comparable across the groups.

You should also use masking to make sure that participants aren’t able to figure out whether they are in an experimental or control group. If they know their group assignment, they may unintentionally or intentionally alter their responses to meet the researchers’ expectations, and this would lead to biased results.

A between-subjects design is also useful when you want to compare groups that differ on a key characteristic. This characteristic would be your independent variable, with varying levels of the characteristic differentiating the groups from each other. There would be no experimental or control groups because all participants undergo the same procedures.

• the first group is aged between 21–30,
• the second group is aged between 31–40,
• the third group is aged between 41–50.

The procedure for all participants is the same: they arrive at the lab individually and perform the reaction time task. Then, you assess age group differences in reaction times.

Between-subjects versus within-subjects design

The alternative to a between-subjects design is a within-subjects design, where each participant experiences all conditions. Researchers test the same participants repeatedly to assess differences between conditions.

There are no control groups in within-subjects designs because participants are tested before and after independent variable treatments. The pretest is similar to a control condition where no independent variable treatment is given yet, while the posttest takes place after all treatments are administered.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

If you use a between-subjects design, you would split your sample into two groups of participants:

• a control group that has a learning session and does an unrelated task for 20 minutes as a counterbalance
• an experimental group that has the learning session, followed by a 20-minute nap.

Then, you would administer the same test to all participants and compare test scores between the groups.

If you use a within-subjects design, everyone in your sample would undergo the same procedures:

1. First, they would all have learning sessions, followed by pretests.
2. Then, they would each take a 20-minute nap.
3. Finally, a posttest would assess their knowledge at the end of the study.

You would compare the pretest and posttest scores statistically.

These two types of designs can also be combined in a single study when you have two or more independent variables.

In factorial designs, multiple independent variables are tested simultaneously. Each level of one independent variable is combined with each level of every other independent variable to create different conditions.

In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

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Pros and cons of a between-subjects design

It’s important to consider the pros and cons of between-subjects versus within-subjects designs when deciding on your research strategy. While a between-subjects design has fewer threats to internal validity, it also requires more participants for high statistical power compared to a within-subjects design.

• Prevents carryover effects

Carryover effects are the lingering effects of being in one experimental condition on a subsequent condition in within-subjects designs. These include practice or learning effects, where exposure to a treatment makes participants’ reactions faster or better in subsequent treatments.

Between-subjects designs also prevent fatigue effects, which occur when participants become tired or bored of multiple treatments in a row in within-subjects designs. Carryover effects threaten the internal validity of a study.

• Shorter duration for the study

In a between-subjects design, each participant is only given one treatment, so every session can be fairly quick.

In contrast, data collection in a within-subjects design takes longer because every participant is given multiple treatments. However, despite the data collection duration per participant taking longer, you need fewer participants compared to between-subjects design.

• Requires more participants and resources

Between-subjects designs require more participants for each condition to match the high statistical power of within-subjects designs.

That means that they also require more resources to recruit a larger sample, administer sessions, and cover costs etc.

• Individual differences may threaten validity

Because different participants provide data for each condition, it’s possible that the groups differ in important ways between conditions, and these differences can be alternative explanations for the results.

To counter this in a between-subjects design, you can use matching to pair specific individuals or groups in your sample. That way, the groups are matched on specific variables (e.g., demographic characteristics or ability level) that may affect the results.

Frequently asked questions about between-subjects designs

What is a factorial design?

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.