What is a within-subjects design? Including pros and cons

By Indeed Editorial Team

Published 19 May 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Developing strong research skills requires that you learn about different approaches to collecting research data or conducting experiments. One of the most common ways to conduct experiments is to use a within-subjects design. Learning about this type of design allows you to plan better experiments, increase the accuracy of your findings and choose what you want your participants to experience. In this article, we answer, 'What is a within-subjects design?, list common pros and cons of it, explain the differences between a within-subjects design and a between-subjects design and explore key research skills that can help you design better experiments.

What is a within-subjects design?

Learning the answer to, 'What is a within-subjects design?' is helpful when you want to improve how you approach and conduct scientific or research experiments. A within-subject design, also known as dependent groups or a repeated measures design, is a method of designing experiments in which you choose to expose all participants to every condition or treatment. This makes the within-subject design the opposite of a between-subjects design where each participant experiences only one condition or treatment.

Related: How to become a data scientist in 4 steps

Pros of a within-subjects design

One of the most important reasons scientists may choose to use the within-subjects design to conduct experiments is that this type of experiment requires a smaller number of participants. Conducting the same experiment using the between-subjects design requires scientists to involve twice as many participants as a within-subjects design. This type of designing experiments also lowers the risk of errors that arise when there are significant differences between participants. Exposing all participants to all treatments or conditions makes observations more accurate.

Related: A brief guide on how to become a research scientist

Cons of a within-subjects design

Before making the decision to use a within-subjects design for your experiment, it's important that you review its disadvantages. One of the major cons of this design is that it increases the chances of a carryover effect. A carryover effect is when one condition that participants are experiencing impacts the performance or behaviour on other conditions. It also may cause participants to feel fatigued, distracted or uninterested, which may fail to produce objective experiment results.

With a within-subjects design, scientists may ask participants to perform certain tasks several times, which may also impact the experiment's results. This is because, by performing the same task several times, participants may start learning how to do it. As a result, scientists may find it hard to determine if the results continued to improve thanks to certain conditions or just because participants figured out and self-learned how to do it.

Within-subjects design vs between-subjects design

Regardless of the type of experiment you're conducting, it's critical that you choose one research method and decide between a within-subjects design and between-subjects design. There are various differences to consider, including:

Transfer of knowledge

A within-subjects design requires that all participants perform all tasks or experience all conditions that you're testing. This means that they may approach later tasks with some knowledge that they gained during their first tasks, which may impact their results. For example, when you're testing usability of several e-commerce websites, after testing several websites, participants may develop a better understanding of how e-commerce works, making it easier for them to navigate the websites they're testing last.

This problem rarely, or never, occurs in between-subjects design. In between-subjects design, each participant is responsible for one task. This means that there's no possibility for them to transfer knowledge to different tasks.

Length of sessions

When using a between-subjects design, you assign just one task to each participant, you also reduce the time of each testing session. Since in between-subjects design participants spend less time in sessions, there's usually not enough time for them to lose interest in the experiment. In a within-subjects design, each participant is responsible for performing all tasks, which means they spend significantly more time on them.

Setting up sessions

Since the within-subjects design requires all participants to experience all conditions, it's essential that they experience them in different order. For example, the first participant may test website A first, website B next and website C at the end. Another user can test website B first, website C next and website A as the last one. This means that you may spend more time on randomising tests. With a between-subjects design, all you can do is to choose one random condition for each participant, which makes setting up sessions easier.

Number of participants

Each participant can only experience one test condition in a between-subjects design, so conducting an experiment using this design requires you to recruit more participants. This is because the more participants you have, the more accurate your results can be. In a within-subjects design, you can work with fewer participants because you can assign multiple tasks or conditions to one participant. In some experiments, this means that you'd recruit twice as many participants if you were to use the between-subjects design.


Depending on your experiment's requirements, choosing one of the two experimental designs may allow you to increase how much you spend on your experiment. There are several things to determine when it comes to costs. Typically, it costs more to recruit more participants, which makes the between-subjects design more expensive. With a within-subjects design, each participant's session is significantly longer, which may make this method more costly if you're renting a lab or paying them on a per-hour basis.

Related: How to calculate variable costs (with components and examples)

Minimising random factors

When conducting a scientific experiment and recruiting participants, it's important that you have control over as many environmental factors as possible. It's also important to remember that there may be some factors you may have no control over, for example, your participants' mood. This makes the within-subjects design more accurate and effective, because it makes it possible for all participants to experience all conditions that you're testing.

How to design an experiment in five steps

Designing an experiment is a complex and multi-step process. Here are the key steps to doing it:

1. Determine your variables

The first step to designing an experiment requires you to determine your variables. This means that you'd start with a specific research question. It's important that you choose a question that you can then translate into your experiment's hypothesis.

2. Choose a hypothesis

After defining what you want to find through your research and experiment, you can write your hypothesis. A hypothesis helps you structure your experiment because it addresses your initial research question. It's important that you choose a hypothesis that is specific and testable.

3. Design conditions

This step involves focusing on the conditions in which you want to conduct the experiment. In other words, how you'd manipulate the independent variable. For example, you can do this by increasing air temperature to see how that affects participants' results.

4. Recruit participants and assign subjects to groups

Before recruiting your participants, it's critical that you decide which experimental design best suits your experiment. The two most common designs include within-subjects and between-subjects design. Depending on the design you choose, you may recruit a different number of participants and assign one or all tasks to each of them.

5. Decide how you'd measure your dependent variable

This last step allows you to decide how you want to measure your results. It's important that you focus on gathering valid and reliable results that are accurate to your employer's situation. You can then use your results to prove or disprove your hypothesis and present your findings to a desired audience, for example, colleagues, customers or clients.

Key research skills

Developing strong research skills may help you advance your career, regardless of your position or industry. Showing your employer that you understand common research methods and can design experiments can even help you change positions within the same company. Here are some key research skills you may develop to design and conduct more accurate scientific or business experiments:

Problem-solving skills

Many research-oriented roles require you to focus on solving complex organisational or scientific problems that organisations and companies may experience. Developing strong problem-solving skills may help you develop innovative solutions to those problems by finding accurate resources and data sources. Problem-solving is also a useful, transferable skill that may help you in your professional and private life, for example, when you're preparing for changing career or relocating to explore better job opportunities in a different country.

Related: Research skills: definition and examples

Attention to details

Attention to details is a key research skill that benefits all scientists and researchers. Having the ability to pay close attention to details can help you identify any irregularities and relationships between variables in your experiments. It also makes it easier to follow strict procedures or regulations that your employer may want you to follow when conducting research or testing participants.

Related: 10 common types of variables in research and statistics

Communicating results

The ability to present and communicate results is essential to making sure your employer or the public can use your findings to improve their processes or change their viewpoint. Communicating your findings often requires you to learn how to visualise data using graphs, tables or charts. It's also essential that you know how to present them using simple language, to make it as accessible to the public as possible.

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