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An Overview of Within-Subject Design

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A within-subject design is a form of experimental design where members are made to experience every treatment or condition. Here, the treatment refers to the various independent variables that are experimental and controlled by the treatment. Sounds confusing? Read more to learn more details.

What is a within-subject design?

One of the initial questions that you must ask when you hear about within-subjects design is what is within-subjects design? Well, within-subject design is that it is a type of experimental design where all the participants are exposed to all types of treatments and conditions.

You might wonder what treatment means; in this case, treatment is a term used to describe several different independent variable levels; the experimenter controls this variable.

For a layman to understand this, all the subjects are treated, considering the variable in question. 

A different independent variable treatment or manipulation is used in each condition when experimenting to assess whether there is a cause-and-effect relationship with the dependent variable. You must also know that within-subject design is also known as dependent groups or repeated measures design. Researchers have compared measures from the same participants between different conditions. 

One of the most important things that you must know is that all participants have to participate in every condition in a within-subjects design.

Difference between within-subject design and between-subjects design?

When you learn about within-subjects designs, you automatically come across the between-subjects design. Most students should know the difference between between-subjects design vs within-subjects design. Participants only come across one condition in a between-subjects design, and different treatment groups are compared.

In between-subject designs, it has control groups and experimental groups or multiple groups that vary on a variable like a gender, ethnicity, test score, etc. Researchers have compared the results of different groups with each other.

If you notice the within-subjects designs, you can see that participants serve as their control by offering baseline scores across different conditions.

The term “within” means you are comparing different conditions inside or within the same group or individual. On the other hand, the term “between” means that you are comparing different conditions between groups.

Here are some examples of within-subjects design vs between-subjects design

Suppose you plan to study whether the college learning environment (that is, your independent variable) affects your test scores (the dependent variable). Again, you have the choice to use either a between-subjects or a within-subjects design.

Now, if you use a between-subjects design, you need to split your sample into two groups of participants:

  • A control group that pursues an on-campus college course,
  • An experimental group that pursues the same college course online.

You need to conduct the same test for all participants and make a comparison of the test scores between the groups.

If you use a within-subjects design, everyone in your sample needs to participate in every condition:

  • Half of the college course is conducted on campus before a test.
  • Half of the college course is conducted online before a comparable test.

You need to recombine the order of the learning environment across the participants. For example, some participants need to first pursue the course on campus before changing to online learning, while the others half need to pursue the course online first before pursuing it in person. Once all these are completed, you have to make a comparison of the test scores within subjects between the two conditions.

Examples of within-subjects design

Example 1: Within-subjects design over time

Consider yourself a social researcher; you are studying the effects of time (the independent variable) on perceptions of the pandemic and coronavirus (the dependent variable). For example, you have gathered a large sample of participants early in 2020 and have sent the same group on an online survey every two to three months since then.

An important question raised in the Likert scale survey that asks participants to rate their fear of getting COVID-19 on a scale from 1–7. To assess the changes in perception, you can compare the differences in survey responses over time within-subjects design.

Example 2: Within-subjects design for different treatments

In this example, think of yourself as a person studying the effects of different messaging styles (this is your independent variable) on generosity (this is your dependent variable). Now every participant here is given 5 different short stories about climate change.

Now each of the stories presents itself using a different tone and style. After finishing each story, participants are asked about their opinion and willingness to donate to a related cause.

To make sure that the participants do not understand the aim and purpose of the study, they are asked several unrelated questions. To gauge the effects of messaging styles on generosity, you can compare the willingness to donate across conditions within subjects.

Disadvantages of a within-subjects design

Within-subjects design is an experimental design that can have advantages in some cases.

However, it has major disadvantages of a within-subjects design that one considers. One of the significant ones of using a within-subject design is that participants taking part in one condition can impact the performance or behaviour on all other conditions, a problem known as a carryover effect.

For example, participants taking part in yoga might impact their later performance in jogging and may even affect their performance on later memory tests.

Fatigue is another significant drawback of using a within-subject design. Participants could feel exhausted, bored, or simply uninterested after taking part in multiple treatments or tests.

Finally, practice effects can also affect performance on the following tests. For example, participants taking part in different levels of the treatment or taking the measurement tests several times can help the participants become more skilled.

This indicates that they may be able to understand how this game is played and how to manipulate the scores and do better on the experiment. This can influence the results and make it challenging to establish the effects due to the different levels of the treatment or simply a result of practice.

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Hi, I am Ethan, a nerd by nature, a data scientist by profession and, a gadget lover at heart. Thanks for stopping by and sparing a few minutes to know me. I would like to let you know, apart from being a Maths lover and a tech freak I have a fetish for English writing. I am into everything that spells maths, technology, and essays. I have an experience of 10 years as a data scientist and that of 5 years as a freelance English essay writer at I like sharing my experiences with people, which further makes me an occasional blogger. 

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