What is a major advantage of a within-subjects design?

In quantitative studies that involve comparisons of conditions or treatments, there are two basic types of designs to consider: between-subjects or within-subjects (also known as repeated-measures). In a between-subjects design, each participant receives only one condition or treatment, whereas in a within-subjects design each participant receives multiple conditions or treatments. Each design approach has its advantages and disadvantages; however, there is a particular statistical advantage that within-subjects designs generally hold over between-subjects designs.

Discover How We Assist to Edit Your Dissertation Chapters

Aligning theoretical framework, gathering articles, synthesizing gaps, articulating a clear methodology and data plan, and writing about the theoretical and practical implications of your research are part of our comprehensive dissertation editing services.

• Bring dissertation editing expertise to chapters 1-5 in timely manner.
• Track all changes, then work with you to bring about scholarly writing.
• Ongoing support to address committee feedback, reducing revisions.

Within-subjects designs have greater statistical power than between-subjects designs, meaning that you need fewer participants in your study in order to find statistically significant effects. For example, the between-subjects version of a standard t-test requires a sample size of 128 to achieve a power of .80, whereas the within-subjects version only requires a sample size of 34 to achieve the same power. This advantage of within-subjects designs might be common knowledge for some students, but many students may not know why this is the case. The answer lies in how variance is divided up (or “partitioned”) in a within-subjects analysis.

Take an analysis of variance (ANOVA) for example. In a between-subjects ANOVA, the total variance is comprised of treatment variance and error variance. You determine if there are differences between groups by examining the proportion of treatment variance to error variance. The error variance in this design can be attributable to individual differences between participants (e.g., demographic differences). In other words, you are trying to see through the “noise” of the variance due to individual differences to see what impact your treatment is having.

However, in a within-subjects ANOVA, we are able to divide up the variance even further. Specifically, we can partition the variance due to individual differences from the rest of the “error” variance. Thus, the total variance in the within-subjects ANOVA is comprised of treatment variance, between-subjects variance (i.e., individual differences), and error variance. We still determine the effect of the treatment by examining the proportion of treatment variance to error variance. By partitioning out the between-subjects variance, we reduce the amount of error variance in the equation, thus reducing the “noise” we have to see through in order to see a significant treatment effect. Put another way, since we are not interested in differences between participants in a within-subjects design, we can throw out the between-subjects variance to get a clearer picture of what is going on in the data.

Having a deeper understanding of the statistical advantages of within-subjects designs may help you make more informed choices about your research design moving forward!

A within-subject design is a type of experimental design in which all participants are exposed to every treatment or condition. It is also known as a repeated measures design.

The term "treatment" is used to describe the different levels of the independent variable, the variable that's controlled by the experimenter. In other words, all of the subjects in the study are treated with the critical variable in question.

This article discusses what a within-subjects design is, how this type of experimental design works, and how it compares to a between-subjects design.

Within-Subjects Design vs. Between Subjects

Let's imagine that you are doing an experiment on exercise and memory. For your independent variable, you decide to try two different types of exercise: yoga and jogging.

Instead of breaking participants up into two groups, you have all the participants try yoga before taking a memory test. Then, you have all the participants try jogging before taking a memory test. Next, you compare the test scores to determine which type of exercise had the greatest effect on performance on the memory tests.

This within-subjects design can be compared to what is known as a between-subjects design. In a between-subjects design, people are only assigned to a single treatment. So one group of participants would receive one treatment, while another group would receive a different treatment. The differences between the two groups would then be compared.

Consider the earlier example of the experiment looking at exercise and memory. In a between-subjects design, one group of participants would do yoga and then take a memory test. A different group of participants would jog and then take the memory test. Afterward, the results of the memory tests would be compared to see how the type of exercise influenced memory.

Recap

In a within-subjects design, all participants receive every treatment. In a between-subjects design, participants only receive one treatment.

Types of Variables in Psychology Research

Advantages of Within-Subjects Design

Why exactly would researchers want to use a within-subject design? One of the most significant benefits of this type of experimental design is that it does not require a large pool of participants.

A similar experiment in a between-subject design, which is when two or more groups of participants are tested with different factors, would require twice as many participants as a within-subject design.

A within-subject design can also help reduce errors associated with individual differences. In a between-subject design where individuals are randomly assigned to the independent variable or treatment, there is still a possibility that there may be fundamental differences between the groups that could impact the experiment's results.

In a within-subject design, individuals are exposed to all levels of a treatment, so individual differences will not distort the results. Each participant serves as their own baseline.

Disadvantages of Within-Subjects Design 

This type of experimental design can be advantageous in some cases, but there are some potential drawbacks to consider. A major drawback of using a within-subject design is that the sheer act of having participants take part in one condition can impact the performance or behavior on all other conditions, a problem known as a carryover effect.

So for instance in our earlier example, having participants take part in yoga might have an impact on their later performance in jogging and may even affect their performance on later memory tests.

Fatigue is another potential drawback of using a within-subject design. Participants may become exhausted, bored, or less motivated after taking part in multiple treatments or tests.

Finally, performance on subsequent tests can also be affected by practice effects. Taking part in different levels of the treatment or taking the measurement tests several times might help the participants become more skilled.

This means they may be able to figure out how to game the results in order to do better on the experiment. This can skew the results and make it difficult to determine if any effect is due to the different levels of the treatment or simply a result of practice.

How Does Experimental Psychology Study Behavior?

Frequently Asked Questions

• What is a 2x2 within subjects design?

  In a 2x2 design, researchers examine how two independent variables with two different levels impact a single dependent variable. For example, imagine a study where researchers wanted to see how the type and duration of therapy influence treatment outcomes. In a 2x2 design, they would examine two types of therapy (cognitive and psychoanalytic) as well as two levels of each treatment (short- and long-term).

• When would you use a within-subjects design?

  A within-subjects design can be a good option if participants or resources are limited. It can also be a good way to examine situations in real-world settings, such as to assess the effectiveness of educational programs.

• When should a within-subjects design not be used?

  If researchers are concerned about the potential interferences of practice effects, they may want to use a between-subjects design instead. Within-subjects designs can also take more time to administer in some cases, so it may be helpful to use a between-sessions design if many participants are available to quickly conduct data collection sessions.

  What is the major advantage of a within

  What is a major advantage of a within-subjects design? It helps us detect the impact of the independent variable.

  What is the major strength of the within subject design?

  exposed to only one level of the independent variable. What is the major strength of the within-subjects design? It guarantees that the participants in the various conditions are equivalent at the start of the study.

  What is the purpose of within subject design?

  Using a within-subjects design. In a within-subjects design, all participants in the sample are exposed to the same treatments. The goal is to measure changes over time or changes resulting from different treatments for outcomes such as attitudes, learning, or performance.

  Which of the following is an advantage of a within

  An advantage of using a within-subjects design is that fewer participants are required overall (economizing) because the same participants are observed in each group. A second advantage of using the within-subjects design is that the test statistic for this design has greater power to detect an effect between groups.