A between-subjects design is a type of experiment that tests different treatment conditions on separate groups of people. After the experiment, researchers compare group outcomes to determine whether they differ. Statisticians also refer to this method as a between-group or independent measures design.
This design likely matches your preconceived ideas about experiments. Each participant experiences only one condition and they are assigned to one group. At the end, you compare group outcomes to see if the treatment caused a difference. For example, did the treatment group have better average health outcomes than the control group?
While a between-subjects design seems logical, it’s crucial to realize that it is just one possible design with its own benefits and drawbacks. For instance, this design is the opposite of a within-subjects design, where the same people experience all conditions. We’ll compare those two designs later.
In this post, you will learn what a between-subjects design is, how it works, when to use it, and its pros and cons.
Learn more about Experimental Designs: Definition and Types.
What Is a Between-Subjects Design?
Use a between-subjects design to compare groups that differ by an attribute of interest. In this type of experiment, you divide participants into separate groups. For instance, one group receives a treatment, while the control group does not. However, you can have multiple groups receiving different treatments.
Ideally, you randomly assign participants to the various experimental groups. Randomization controls confounders by equalizing the groups at the beginning, which helps you conclude that the treatment likely caused any group differences you observe at the end.
The attribute or treatment you vary between the groups is the independent variable and the outcome of interest is the dependent variable. After the experiment, you compare the outcomes between groups. If a group performs differently, the treatment might be the cause.
Learn more about Independent vs. Dependent Variables: Differences & Examples.
Example of Between-Subjects Design
Suppose you want to test whether a new welcome video boosts job application rates.
You gather 120 people and randomly split them into two groups:
- One group sees the current welcome video.
- The other group sees the new version.
Everyone views the website and chooses whether to apply. You then compare application rates between groups.
This setup is a classic between-subjects design example. Each group sees only one version of the video. If the second group applies more often, the new video might be working.
Using Control and Experimental Groups
In many between-subjects experiments, you assign participants to:
- A control group that receives no treatment or a placebo.
- An experimental group that gets the treatment you’re testing.
This process helps you isolate the effect of your independent variable—the thing or treatment you’re changing.
For stronger results, use random assignment. That helps ensure your groups are similar before the treatment begins.
You should also consider blinding, where participants don’t know their group assignment. This strategy helps reduce bias, such as people trying to behave how they think researchers want.
Other sources of bias include the following:
Randomized controlled trials (RCTs) are the gold standard of between-subjects designs. They include rigorous design features that reduce bias and boost internal validity to support strong causal conclusions.
Learn more about Correlation vs. Causation: Understanding the Differences.
Comparing Natural Groups in a Between-Subjects Design
Not all between-subjects designs involve treatments. Sometimes, researchers use this design to compare natural groups that differ on a key variable.
Example Without Experimental Treatment
Suppose you want to know if reading speed differs by preferred language.
You group participants by their self-reported first language:
- English
- Spanish
- Mandarin
All participants complete the same reading task. You then compare scores between groups.
There’s no control or experimental group. The groups differ naturally, and you compare their results using a between-subjects design.
Between-Subjects vs Within-Subjects
A between-subject design has groups that contain different participants and each person receives one treatment or control condition.
In contrast, a within-subjects design does not have separate groups. Instead, this method exposes each participant to every treatment condition and compares their outcomes across conditions. In some designs, researchers measure a baseline before the treatments, while in others, participants experience all conditions in a random or balanced order.
A critical benefit for within-subjects design is that each person acts as their own control, which reduces experimental error and can dramatically increase statistical power. However, this approach can lead to carryover effects if one condition influences performance in the next.
Comparing the Two Designs
Imagine you want to test whether standing desks improve productivity.
With a between-subjects design:
- Group A works at a regular desk.
- Group B works at a standing desk.
- You compare output between the groups.
With a within subjects design:
- Everyone works one week at a regular desk.
- Then, they work one week at a standing desk.
- You compare their productivity week to week to their baseline.
Each method has strengths and weaknesses. Learn more about a Within-Subjects Design in Experiments Explained.
When to Use a Between-Subjects Design
Here are some reasons you might choose this design:
Avoid Carryover Effects
In within-subjects designs, earlier experiences can affect later results. People might improve just from practice—or get tired.
Between-subjects designs avoid this. Each participant experiences only one version of the treatment.
Save Time Per Participant
In a between-subjects study, sessions are short. You test each person once.
This brevity makes scheduling easier—especially if tasks are long or demanding.
When Comparing Natural Groups
Sometimes, your groups already differ on a key variable. A between-subjects design lets you compare those groups without giving anyone a treatment.
When You Have Enough Participants
This design needs more participants to reach the same statistical power as within-subjects designs. Learn more about Statistical Power.
Downsides of a Between-Subjects Design
While between-subjects designs offer many benefits, they also have a few critical trade-offs. Here are the main drawbacks to consider.
You Need More Participants and Resources
A between-subjects design requires more people than a within-subjects design to obtain high statistical power. Each group needs enough participants to detect differences confidently.
That also means:
- More time spent recruiting participants.
- More resources for materials, space, and data collection.
- Higher costs overall.
These higher expenses can be a significant limitation when you’re working with a limited budget or a small sample pool.
Individual Differences Can Affect Validity
In a between-subjects design, each group contains different people. That allows individual differences—like personality, background, or skill level—to influence the results.
These differences can act as confounding variables, providing alternative explanations for your findings. Even with random assignment, you might end up with groups that differ in meaningful ways, especially if your sample size is small.
This variability can lower the internal validity of your study and make it harder to interpret your results.
To reduce this risk, researchers often:
- Use random assignment to distribute characteristics evenly.
- Apply matching to pair participants across groups with similar traits (like age or ability).
- Control for key variables in the analysis.
On the other hand, a within-subjects design controls for individual differences by making the participants their own controls.
External Factors Must Be Controlled
Each group in a between-subjects design must go through the study under the same conditions. If one group completes the task on a different day, in a different location, or with a different researcher, those changes might influence their responses.
Consistency matters. In a between-subjects design, even small differences in how the study is conducted can bias the results.
FAQ: Between-Subjects Design
What is a between-subjects design in psychology?
In psychology, a between-subjects design compares different groups of people who each experience only one condition. Researchers use it to test how an independent variable affects behavior or outcomes across groups.
What is an independent groups design in psychology?
An independent groups design is another name for a between-subjects design. It emphasizes that the groups are separate and that measurements are taken from different individuals in each condition.
What does between-subjects design mean in statistics?
In statistics, a between-subjects design means that data is collected from different people in each experimental group. This allows researchers to compare group means and test for significant differences.
Is between-subjects the same as independent measures?
Yes. A between-subjects design, independent measures design, and independent groups design all refer to the same concept: testing different conditions using separate groups of participants.
When should I use a between groups design?
Use a between groups design when you want to avoid carryover effects, compare natural groups, or test treatments without repeating them for each person.
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