Between-Subjects Design | Examples, Pros & Cons

Research Studies

• Baeyens, Diaz, & Ruiz (2011) investigated the resistance to extinction of evaluative conditioning using separate groups of participants, which is a between-subjects approach.
• Ehrlichman et al. (2007) studied the modulation of startle reflex by pleasant and unpleasant odors, comparing the effects between different groups of subjects.
• Carey, Lester, & Valencia (2016) examined the effects of a fatal vision goggles intervention on attitudes toward drinking and driving and texting and driving among middle school children, using a between-subjects design to compare the intervention group to a control group.
• Chang and Kang (2018) investigated the impact of the 2018 North Korea-United States Summit on South Koreans’ altruism toward and trust in North Korean refugees, using a between-subjects design to compare responses before and after the summit.
• Egele, Kiefer, & Stark (2021) compared the faking of self-reported health behavior between a within-subjects and a between-subjects design, highlighting the use of both approaches in their study.

Between-subjects vs within-subjects design

In a between-subjects design, different groups of participants are exposed to different conditions, and the results are compared between these groups.

In contrast, a within-subjects design exposes each participant to all conditions, and the results are compared within the same group of participants.

The pretest in a within-subjects design serves as a baseline, similar to a control condition, while the posttest assesses the effects of the independent variable treatments.

Example: Between-subjects vs within-subjects design

You’re planning to study whether listening to classical music (your independent variable) while studying can improve memory retention (your dependent variable).

You can use either a between-subjects or a within-subjects design.

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

• A control group that studies in silence for 30 minutes
• An experimental group that studies while listening to classical music for 30 minutes

Then, you would administer the same memory test to all participants and compare the 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 study a list of words in silence for 30 minutes and take a memory test.
2. After a break, they would study a new list of words while listening to classical music for 30 minutes.
3. Finally, they would take another memory test on the second list of words.

You would compare the memory test scores from the silent condition and the classical music condition 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.

For example, you could study the effects of both music (classical vs. no music) and study environment (library vs. café) on memory retention. This would create four conditions:

1. Classical music in library
2. Classical music in café
3. No music in library
4. No music in café

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

For instance, you could have two groups of participants (between-subjects: classical music vs. no music) who each study in both the library and the café (within-subjects: study environment).

Advantages

Eliminates order effects

Order effects refer to the influence of the sequence or order in which conditions are presented on the results

In within-subjects designs, the order of conditions can sometimes influence the results (e.g., practice effects, boredom). Between-subjects designs eliminate this concern by having each participant only experience one condition.

Avoids carryover effect

Carryover effects refer to the influence of one experimental condition on a participant’s behavior or responses in a subsequent condition.

For example, if a participant learns a new skill in one condition, that learning might “carry over” and improve their performance in a later condition, even if that condition isn’t designed to teach that skill.

However, in between-subjects study designs, the participants are divided into different treatment groups, so one participant’s exposure to treatment will not affect the outcome of a subsequent condition.

It’s important to note that between-subjects designs don’t necessarily eliminate all types of carryover effects. There could still be spillover between groups if participants in different conditions interact and share information, for instance.

Short and straightforward

Each participant is only assigned to one treatment group, so the experiments tend to be uncomplicated. Scheduling the testing groups is simple, and researchers tend to be able to receive and analyze the data quickly.

Reduced testing fatigue

Since each participant is only tested in one condition, between-subjects designs can help avoid testing fatigue that might occur in within-subjects designs where participants go through multiple conditions in one session.

Limitations

A large participant pool is necessary

Because each subject is assigned to only one condition, this type of design requires a large sample. Thus, these studies also require more resources and budgeting to recruit participants and administer the experiments.

Individual differences

Differences between subjects within a given condition may be an explanation for results, introducing error and making the effects of an experimental condition less accurate.

Requires careful matching or random assignment

To help control for individual differences between groups, researchers must carefully match participants on key characteristics or use random assignment to conditions.

If groups differ from the outset, it can confound the results.

Less statistical power

For the same sample size, between-subjects designs have less statistical power than within-subjects designs.

This means larger effect sizes are needed to detect significant differences between conditions, or larger sample sizes are required.

Frequently Asked Questions