In the realm of research, choosing the right methodology is pivotal for obtaining meaningful results. The within-subject design is one such methodology where participants are exposed to all experimental conditions, allowing for direct comparisons of their responses. Within-subjects design can be useful in experimental research, especially when there is a limited sample size or individual differences between participants must be controlled.
Within-Subjects Design – In a Nutshell
- A within-subjects design is a research design in which each participant is exposed to all independent variable levels, allowing for direct comparisons.
- Advantages of a within-subjects design include increased statistical power, control for individual differences, and the potential for reduced error variance.
- Limitations of a within-subjects design include the potential for order effects, carryover effects, and practice effects.
Definition: Within-subjects design
A within-subjects design, also known as one dependent group, is a research design in which each participant serves as their own control and is exposed to all levels of the independent variable. This means that participants are tested in all study conditions, rather than randomly assigned to only one condition.
The within-subjects design is the equivalent of one between-subjects design (individual participants are offered a singular condition). The word “within” in within-subjects design refers to the fact that the comparisons made are within the same subject or participant rather than between different groups of participants.
It is also referred to as repeated measures design or crossover design since researchers compare aspects of the same participants in varied conditions.
Within-subjects design: How it works
In a within-subjects design, all participants are drawn from a single sample and are exposed to identical conditions to measure changes over time or due to different conditions (also known as “treatments”). This design can be used to examine a variety of variables, such as opinions or performance.
Example
Multiple conditions in a within-subjects design
- You want to determine the impact of different colors (the independent variable) on mood (the dependent variable).
Each participant is placed in four differently coloured rooms. - There are different shades of each color.
The participants are observed and asked what they think of the color in the room and how it affects their mood. - The interviewers ask other unrelated questions to prevent the participants from identifying the investigation’s purpose.
You can test the effect of the colors on the mood of the participants by comparing the outcomes of the various conditions.
Note: When studying various conditions using the same group of participants, it is important to randomize the order in which the conditions are presented (known as counterbalancing).
This helps to eliminate the possibility that the effects of previously presented conditions may impact the results of the later conditions. In other words, the order in which the conditions are presented is not a factor that affects the results. Randomization involves using a wide range of different sequences for presenting the conditions, whereas counterbalancing involves only a few sequences.
Within-subjects design vs. between-subjects design
A between-subjects design is the opposite of a within-subjects design, as each participant is subjected to only one condition, and the group means are then compared.
| Within-subjects design | Between-subjects design |
| participants are tested in all study conditions | each participant is subjected to only one condition |
| In contrast, within-subjects designs simultaneously involve the participants in a control group as a guideline measurement is taken. This baseline measurement is then observed against other conditions. | Typically, between-subjects designs comprise a control group, which does not receive any manipulation, and singular or multiple experimental groups that vary in one particular variable. These may include gender, ethnicity, or key scores. Researchers can collate the results of the different groups. |
Note: “Within” in this context refers to comparing different conditions in the same group or individual, while “between” involves comparing conditions across several individuals or groups.
You can apply both within-subject and between-subject designs in some cases, for instance, if you’re investigating whether e-commerce sites (independent variable) influence levels of consumer spending (dependent variable).
Example
For a between-subject design, you can group respondents into two groups:
- A group who shops in physical stores
- An experimental group who shops on e-commerce websites
You would shop in both ways with all participants and compare the spending of the control group with that of the experimental group.
Example
If you used a within-subject design, each participant would be offered all conditions:
- Half the time is spent on an e-commerce website, followed by shopping.
- The other half of the time is spent in a physical shop, followed by shopping.
To compare the spending habits of participants between online and in-person shopping experiences, it’s important to randomize the order in which participants shop. This means some participants would start shopping on an e-commerce site, while others would start at a physical shop.
Comparing the spending of the same participants in both conditions makes it possible to assess whether the shopping experience affects spending habits.
Counterbalancing can be a more convenient option for researchers, as every possible order in which the conditions are presented occurs equally often. For instance, if four participants are presented with the order XYZ, four in the order ZXY, and four who are offered XZY.
Within-subjects design: Pros and Cons
The within-subjects design has advantages and disadvantages, which can affect the validity and reliability of the results.
Within-subjects designs are suitable for establishing correlations or causality between variables, even with small sample sizes. However, it could also compromise internal validity.
✓ Advantages
-
Smaller samples
A within-subjects design typically requires fewer participants than a between-subjects design, making it easier to find enough participants. Each participant is subjected to multiple tests under different conditions, making the design more efficient. -
Identifying participant differences
In a between-subjects design, the participants are only exposed to one condition, resulting in variations in individual properties such as income level and race. Determining whether the differences found were caused by the alteration or participant characteristics is challenging. -
Greater statistical power
A within-subjects design has higher statistical power due to the absence of individual variation. It requires half the number of participants of a between-subjects design to attain the same level of statistical power.
✗ Disadvantages
-
Time/order effects
Where the order in which participants experience the conditions affects their performance. For example, participants may improve their performance over time due to practice effects or become fatigued due to the length of the study, which can affect the results. -
Carryover effects
Where the effects of one condition carry over into subsequent conditions. This can happen when a participant’s performance in one condition is influenced by their experience in the previous condition, which may confound the results.
FAQs
A within-subjects design is a research design in which each participant is exposed to all levels of the independent variable, allowing for a direct comparison of the effects of each level.
In a within-subjects design, the same participants are tested under all conditions, allowing researchers to control for participant properties.
The within-subjects design allows for higher statistical power and requires fewer participants, but it is vulnerable to carryover and order effects, which can compromise the internal validity of the study.
Researchers can control for order effects by counterbalancing the order in which the different levels of the independent variable are presented to participants.
