What is within-subjects study design?

Choosing the right experiment design is an important aspect of research, as it determines the structure and organization of the study. It can also have a significant impact on the reliability and validity of the results.

Understanding the options available to you is the first step in choosing the right design. In this article, we'll be taking a detailed look at within-subjects design, and comparing it to between-subjects design.

Within-subjects design, also known as repeated measures design, is a type of experimental design in which the same participants are tested under multiple conditions or points in time. This allows researchers to directly compare the responses of each individual, rather than relying on group averages as in between-subjects designs.

In a within-subjects design, the same group of participants is tested under all conditions, so there's no need to worry about potential differences between groups that could confound the results. This makes it easier to control extraneous variables and increases the power of the study, since the same participants serve as their own controls.

For example, if a researcher is interested in the effect of a new medication on blood pressure, they could use a within-subjects design by measuring blood pressure in the same group of participants both before and after taking the medication. 

By comparing the blood pressure of each individual before and after taking the medication, the researcher can directly assess the effect of the medication on blood pressure without having to worry about differences between groups of participants.

Although every experiment should be designed according to its own unique set of criteria, below are the basic steps involved in using a within-subjects design.

1. Define the research question - The first step in using a within-subjects design is to clearly define the research question and determine the specific variables of interest.

2. Select the participants - The next step is to select the participants for the study. It's important to carefully consider the inclusion and exclusion criteria and to ensure that the sample is representative of the population of interest.

3. Determine the conditions or time points - The next step is to determine the conditions or time points that will be tested in the study. Researchers must carefully consider the potential confounding and extraneous variables that may affect the results, and therefore design the study in a way that controls for these variables as much as possible.

4. Administer the measures - The next step is to administer the measures to the participants under each condition or time point.

5. Analyze the data - The final step is to analyze the data using appropriate statistical analyses to test the research hypotheses and draw conclusions about the results. 

A specific UX example of the differences between within-subjects design and between-subjects design can be illustrated through a typical A/B testing scenario. For a within-subjects study, the same group of participants would be shown both A and B variations. For a between-subjects design study, the participants would be separated into two different groups with one being shown the A variation, while the other is shown the B variation.

Researchers often find themselves choosing from a between-subjects design and a within-subjects one. These two options are opposites in many ways, and making the correct choice means understanding the unique differences between the two, as well as their strengths and weaknesses. 

Between-subjects design, also known as independent groups design, is a type of experimental design in which different groups of participants are tested under different conditions or at different time points. This means that each participant is only tested under one condition, and the results are compared across the different groups that have been tested.

The between-subjects equivalent to our previous blood-pressure study example looks like this: Researchers randomly assign participants to either a treatment group or a control group, and measure blood pressure in both groups before and after taking the medication. 

By comparing the average blood pressure of the two groups, the effect of the medication on blood pressure can be assessed, but researchers cannot directly compare the blood pressure of individual participants.

To help you better understand how between-subjects design compares to within-subjects design, let's take a look at the pros and cons of the former. Then, we'll take a closer look at how to choose between them.

Pros

• Between-subjects design is generally more suitable for studying between-subjects differences, such as the effects of different treatments or the influence of individual characteristics on a response.

• This design allows researchers to control for many extraneous variables and reduce the influence of individual differences on the results.

• Between-subjects design is often more feasible and ethical than within-subjects design, especially when it is not possible or ethical to randomly assign participants to different conditions.

Cons

• Between-subjects design does not allow researchers to directly compare the responses of individual participants, which may reduce the power of the study and limit the ability to detect small but meaningful changes.

• This design is vulnerable to confounding variables, such as individual differences in age, gender, and background, which may influence the results and make it difficult to interpret the findings.

• Between-subjects design may require a larger sample size to achieve the same level of statistical power as within-subjects design, which can be more time-consuming and expensive to implement.

When it comes time to choose the design that meets your study’s needs, a good rule of thumb is to determine whether the differences you're looking to study are between subjects or within subjects. 

Between-subjects design is generally more suitable for studying between-subjects differences, such as the effects of different treatments or the influence of individual characteristics on a response. This design is particularly useful when it is not feasible or ethical to randomly assign participants to different conditions, allowing researchers to control for certain variables and reduce the influence of individual differences on the results.

Within-subjects design, on the other hand, is generally more suitable for studying within-subjects changes or differences, such as the effects of a treatment over time or the difference between two closely related conditions. This design is particularly useful when it is important to control for extraneous variables and eliminate between-subjects variability, allowing researchers to directly compare the responses of individual participants and increasing the power of the study.

Regardless of the design you choose, randomization is an important principle. It helps to control for extraneous variables and reduce the influence of individual differences on the results. 

In a between-subjects design, randomization helps to control for extraneous variables that may differ between the groups, such as age, gender, and background. By randomly assigning participants to different groups, researchers can reduce the risk of systematic bias and increase the validity of the study.

In a within-subjects design, randomization can be used to control for order effects, which refer to changes in the response of participants due to the order in which they are tested. For example, if a researcher is studying the effect of a treatment on anxiety, they could use a within-subjects design and randomly assign the order in which the treatment and control conditions are presented to each participant. This helps to control for potential order effects and reduces the risk of systematic bias.

When deciding the design of your experiments, it's important to understand the strengths and weaknesses of the options available to you. The following advantages make within-subjects design a good option.

• More statistical power – Because the same participants are used as their own controls, within-subjects designs have higher statistical power than between-subjects designs, which means they are more likely to detect real effects if they exist.

• Less variability – By using the same participants in all conditions, within-subjects designs eliminate between-subjects variability, which can be a major source of noise in the data. This allows researchers to detect small but meaningful changes in the response of each individual.

• Improved control – Within-subjects designs allow researchers to control for many extraneous variables, such as individual differences in age, gender, and background, which can confound the results in between-subjects designs.

• Greater efficiency – Within-subjects designs are generally more efficient than between-subjects designs, as they require fewer participants to achieve the same level of statistical power. This can be particularly useful when studying rare or hard-to-recruit populations.

• Increased feasibility – Within-subjects designs can be more feasible than between-subjects designs in some cases, especially when it is difficult or impossible to randomly assign participants to different conditions.

Although the within-subjects design is a great choice for many types of experiments, it doesn't fit all of them. For those it does fit, there are also limitations that researchers should be aware of to improve the design of their study.

• Order effects – Within-subjects design is vulnerable to order effects, which refer to changes in the response of participants due to the order in which they are tested. Order effects can be controlled through the use of randomization, but it is important to carefully consider their potential impact on the results.

• Practice effects – Within-subjects design may also be vulnerable to practice effects, which refer to improvements in performance due to repeated testing. Practice effects can be controlled through the use of appropriate counterbalancing and statistical analyses, but it is important to carefully consider their potential impact on the results.

• Fatigue – Within-subjects design may be more prone to participant fatigue than between-subjects design, as participants are being tested multiple times and may become tired or bored. This can affect the quality of the data and make it difficult to interpret the results.

• Data analysis – Within-subjects design requires more sophisticated statistical analyses to account for the repeated measures and the potential within-subjects correlations in the data. This may require more specialized knowledge and expertise and may be more challenging to interpret than between-subjects design.

Within-subjects design should be used when researchers are interested in studying within-subjects changes or differences, such as the effects of a marketing effort over time or the difference between two closely related screen layouts.

The appropriate statistical test for a within-subjects design depends on the specific research question and the type of data being collected. This may include paired t-tests, repeated measures ANOVA, or mixed-effects models.