Within Subjects Design in Experiments Explained

What Is a Within-Subjects Design?

A within-subjects design is an experimental design where each participant experiences all treatment conditions in the experiment. The analysis compares how the same person performs under different conditions. This experimental design is also called a dependent group or repeated measures design.

When most people think of an experiment, they imagine splitting participants into separate groups, giving each group a single treatment, and comparing the group results at the end. That’s a between-subjects design.

However, a within-subjects design works very differently—and it offers powerful benefits. In this type of study, you measure each participant multiple times, once under each treatment condition of your independent variable. You then compare the outcomes “within the subjects” to see how the various treatments affect each person.

This approach of exposing participants to multiple conditions might not match your mental image of a typical experiment, but it has significant advantages.

The key benefit of this design is that each participant serves as their own control. This setup reveals how each person responds to all conditions. That information allows the statistical model to account for how individual differences affect the outcome, which can dramatically increase statistical power.

In other words, if a treatment effect exists, a within-subjects design is more likely to detect it than a between-subjects design with the same sample size. Or, you can use smaller samples without sacrificing power.

In this post, you will learn how a within-subjects design works and see examples, when to use it, how it compares to a between-subjects design, and why it’s a valuable option for many types of research.

Learn more about Experimental Designs: Definition and Types.

Example of Within-Subjects Design

Suppose you want to test whether background music affects productivity.

You gather 30 participants. Each person completes a 30-minute work session under three different conditions:

• No music
• Classical music
• Pop music

Each participant completes all three sessions on separate days. You measure the number of tasks completed in each session and compare results within each person.

This example is a classic within-subjects design. Every person experiences all conditions, so the comparison happens at the individual level.

Randomizing and Counterbalancing Treatment Order

One challenge with within-subjects designs is that the order of conditions can affect the results.

For example, if everyone experiences the no-music session first, they might perform worse due to unfamiliarity with the task. The researchers can’t determine whether it was the lack of familiarity or no music that produced the low performance.

To address this, researchers use counterbalancing or randomization.

• Randomizing the order means each participant experiences the treatments in a random sequence.
• Counterbalancing ensures that each possible order of treatments is represented equally across participants.

These strategies help prevent order effects, like fatigue, learning, or boredom, from biasing the results.

Example: Counterbalancing Treatment Order

Suppose you’re studying how three different types of instructional videos—lecture-style, animated, and interactive—affect student comprehension.

Each participant watches all three videos, completing a short quiz after each one. To prevent the order of presentation from influencing quiz performance (due to practice or fatigue), you use counterbalancing.

You randomly assign participants to different groups where the video order varies:

• Group 1: Lecture → Animated → Interactive
• Group 2: Animated → Interactive → Lecture
• Group 3: Interactive → Lecture → Animated

This method ensures that each video appears equally often in each position, helping you isolate the effect of the video style from the effect of its position in the sequence.

Using Within-Subjects Designs in Longitudinal Studies

While many within-subjects designs involve controlled treatments presented, others focus on tracking natural changes over time in longitudinal studies.

In longitudinal research, time is the independent variable, even though it can’t be manipulated. The goal is to observe how an outcome—the dependent variable—changes within individuals across multiple time points.

Some longitudinal studies involve no experimental treatment. Others may include an intervention introduced at a certain point, making it possible to study how people respond to it over time.

Example: Tracking Behavioral Change Over Time

Suppose you’re studying how people’s physical activity levels change after getting a fitness tracker.

You recruit 75 participants and measure their activity levels each week for six months. For the first month, they track their habits without any device. In the second month, you introduce a fitness tracker. For the remaining months, you observe how their activity evolves.

It is a within-subjects longitudinal design because you’re measuring the same people repeatedly and analyzing how their responses change over time. This approach allows you to see short-term and long-term effects—whether or not you’ve introduced a treatment.

Within-Subjects vs Between-Subjects

Here’s how the two experimental designs differ:

Feature	Within-Subjects Design	Between-Subjects Design
Treatment conditions experienced	All	One
Sample size needed	Smaller	Larger
Controls for individual differences?	Yes	No
Risk of carryover effects	High	Low

Comparative Example

Suppose you want to study whether different lighting types affect reading comprehension.

With a within-subjects design:

• Each participant reads three passages under different lighting conditions: warm light, cool light, and natural light.
• After each session, they take a comprehension quiz.
• You compare each participant’s quiz scores across all three lighting conditions.

Because each person experiences all treatments, this experiment uses a within-subjects design.

With a between-subjects design:

• You split participants into three separate groups.
• Each group experiences only one lighting condition while reading.
• You compare quiz scores between the groups to assess the effect of lighting.

Each method can test the same question—but the within-subjects design uses fewer participants and accounts for individual differences.

Learn more about a Between-Subjects Design in Experiments Explained.

When to Use a Within-Subjects Design

Here are some situations where a within-subjects design makes sense:

You Want to Control for Individual Differences

Because each person acts as their own control, this design reduces the effect of participant variability—such as ability, mood, or motivation.

This control leads to more precise comparisons and helps isolate the treatment effect.

You Have a Small Sample Size

Within-subjects designs use fewer participants than between-subjects designs to reach the same level of statistical power.

This efficiency is ideal when recruiting a large sample is difficult or costly.

You’re Measuring Change Over Time

If your study tracks changes across time—before and after a treatment or across multiple sessions—a within-subjects design is a natural fit.

Downsides of a Within-Subjects Design

Despite its strengths, this design also has drawbacks:

Carryover Effects

Earlier conditions can influence performance in later ones. These include:

• Practice effects: Participants improve from repetition, not treatment.
• Fatigue effects: Participants lose focus as the session progresses.
• Boredom or disengagement: Especially in longer studies.

Randomization and counterbalancing help reduce these effects but can’t always eliminate them.

Long Study Sessions

Because participants experience every condition, sessions tend to be longer. The length can increase the risk of fatigue and reduce participation rates.

Not Always Feasible

Some treatments can’t be repeated or undone. For example, in a surgery study, a participant can’t try another treatment after receiving the first.

In these cases, a between-subjects design is the only option.

Analyzing Within-Subjects Designs

Analyzing within-subjects designs requires different statistical techniques than between-subjects designs.

Because each participant provides multiple measurements, the data points are not independent. Standard ANOVA methods that analysts use for between-groups designs don’t account for these dependent samples. Learn about independent vs. dependent samples.

Instead, researchers typically use repeated measures ANOVA or linear mixed-effects models. These methods include random effects to account for variability between participants. Repeated measures ANOVA is an older method for analyzing within-subjects data. While analysts still use it in simple designs, they have largely replaced it with the more flexible linear mixed-effects models—especially as statistical software has improved.

This structure allows you to:

• Model the correlation between repeated measurements.
• Account for individual baselines and patterns.

These two points help avoid inflated Type I error rates by properly modeling the dependence between repeated observations and accounting for individual variability in baseline levels and treatment responses.

If you’re familiar with traditional ANOVA, analyzing a within-subjects design builds on that foundation—but adds complexity to handle the repeated nature of the data.

See an example of a repeated measures ANOVA with experimental data.

FAQ: Within-Subjects Design

What is a within-subjects design in psychology?

In psychology, a within-subjects design measures how the same person performs across different conditions. Researchers use this method to reduce the impact of individual differences.

What is a repeated measures design?

A repeated measures design is another name for a within-subjects design. It means you collect multiple data points from the same person under different conditions.

Why is a within-subjects design more powerful?

This design reduces error by controlling for differences between participants. That leads to higher statistical power, even with fewer participants.

When should I avoid using this design?

Avoid it when carryover effects are likely or when treatments can’t be repeated. In those cases, use a between-subjects design instead.

What is counterbalancing in within-subjects design?

Counterbalancing is when you vary the order of treatments across participants to prevent order effects from biasing results.