Between-Subjects Design | Examples, Pros & Cons

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, also called 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 identity, 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 (e.g., a placebo to control for a placebo effect).

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.

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. This can lead to a few types of research bias, particularly social desirability bias, self-selection bias, a Hawthorne effect, or other demand characteristics.

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.

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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.

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.

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.

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Frequently asked questions about between-subjects designs

What’s the difference between within-subjects and between-subjects designs?

In a between-subjects design, every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design, each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

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.

What are the pros and cons of a between-subjects design?

While a between-subjects design has fewer threats to internal validity, it also requires more participants for high statistical power than a within-subjects design.

Advantages:

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.

Disadvantages:

• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Can you use a between- and within-subjects design in the same study?

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

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.