In a between-subjects design, each participant is assigned to only one one level of the independent variable (treatment condition), and researchers will compare group differences between participants in these various conditions.
How to Use
In a between-subjects design, participants are divided into separate groups, each experiencing a different condition or level of the independent variable.
One of these groups is often a control group, which receives no treatment or a placebo, while the other groups are experimental groups that receive different levels of the treatment or intervention.
Studies can include multiple experimental groups, each receiving a different level or type of treatment. For example, a study investigating the effects of different doses of a drug might have three groups: a control group receiving a placebo, a low-dose group, and a high-dose group.
The goal of a between-subjects design is to compare the outcomes (dependent variable) between these different groups to determine if the varying levels of the independent variable lead to significant differences in the results.
By manipulating the independent variable between groups, researchers can assess the effectiveness of different treatments or interventions.
Minimizing Bias
To minismize bias, the participants should be randomly assigned to either the control group or one of the experimental conditions. They should not know which group they are assigned to.
Blinding (masking) in between-group designs prevents participants from knowing their group assignment. This reduces expectancy effects, demand characteristics, and placebo effects that could bias results.
By keeping subjects unaware of their condition, researchers can more confidently attribute any observed differences to the actual treatment effect rather than participant expectations or behavior changes.
Example
To test whether a new meditation app (your independent variable) can reduce anxiety levels (your dependent variable), you gather a sample of 100 participants who report high levels of anxiety.
You use a between-subjects design to divide the sample into two groups:
- A control group where the participants are instructed to continue their daily routines without using the meditation app,
- An experimental group where the participants are instructed to use the meditation app for 20 minutes daily for 4 weeks.
Before and after the 4-week period, you administer an anxiety assessment to all participants.
Then, you compare the change in anxiety levels between the two groups using statistical analysis to determine if the meditation app had a significant effect on reducing anxiety.
Research Studies
- Baeyens, Diaz, & Ruiz (2011) investigated the resistance to extinction of evaluative conditioning using separate groups of participants, which is a between-subjects approach.
- Ehrlichman et al. (2007) studied the modulation of startle reflex by pleasant and unpleasant odors, comparing the effects between different groups of subjects.
- Carey, Lester, & Valencia (2016) examined the effects of a fatal vision goggles intervention on attitudes toward drinking and driving and texting and driving among middle school children, using a between-subjects design to compare the intervention group to a control group.
- Chang and Kang (2018) investigated the impact of the 2018 North Korea-United States Summit on South Koreans’ altruism toward and trust in North Korean refugees, using a between-subjects design to compare responses before and after the summit.
- Egele, Kiefer, & Stark (2021) compared the faking of self-reported health behavior between a within-subjects and a between-subjects design, highlighting the use of both approaches in their study.
Between-subjects vs within-subjects design
In a between-subjects design, different groups of participants are exposed to different conditions, and the results are compared between these groups.
In contrast, a within-subjects design exposes each participant to all conditions, and the results are compared within the same group of participants.
The pretest in a within-subjects design serves as a baseline, similar to a control condition, while the posttest assesses the effects of the independent variable treatments.
Example: Between-subjects vs within-subjects design
You’re planning to study whether listening to classical music (your independent variable) while studying can improve memory retention (your dependent variable).
You can use either a between-subjects or a within-subjects design.
If you use a between-subjects design, you would split your sample into two groups of participants:
- A control group that studies in silence for 30 minutes
- An experimental group that studies while listening to classical music for 30 minutes
Then, you would administer the same memory test to all participants and compare the scores between the groups.
If you use a within-subjects design, everyone in your sample would undergo the same procedures:
- First, they would all study a list of words in silence for 30 minutes and take a memory test.
- After a break, they would study a new list of words while listening to classical music for 30 minutes.
- Finally, they would take another memory test on the second list of words.
You would compare the memory test scores from the silent condition and the classical music condition statistically.
These two types of designs can also be combined in a single study when you have two or more independent variables.
In factorial designs, multiple independent variables are tested simultaneously. Each level of one independent variable is combined with each level of every other independent variable to create different conditions.
For example, you could study the effects of both music (classical vs. no music) and study environment (library vs. café) on memory retention. This would create four conditions:
- Classical music in library
- Classical music in café
- No music in library
- No music in café
In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.
For instance, you could have two groups of participants (between-subjects: classical music vs. no music) who each study in both the library and the café (within-subjects: study environment).
Advantages
Eliminates order effects
Order effects refer to the influence of the sequence or order in which conditions are presented on the results
In within-subjects designs, the order of conditions can sometimes influence the results (e.g., practice effects, boredom). Between-subjects designs eliminate this concern by having each participant only experience one condition.
Avoids carryover effect
Carryover effects refer to the influence of one experimental condition on a participant’s behavior or responses in a subsequent condition.
For example, if a participant learns a new skill in one condition, that learning might “carry over” and improve their performance in a later condition, even if that condition isn’t designed to teach that skill.
However, in between-subjects study designs, the participants are divided into different treatment groups, so one participant’s exposure to treatment will not affect the outcome of a subsequent condition.
It’s important to note that between-subjects designs don’t necessarily eliminate all types of carryover effects. There could still be spillover between groups if participants in different conditions interact and share information, for instance.
Short and straightforward
Each participant is only assigned to one treatment group, so the experiments tend to be uncomplicated. Scheduling the testing groups is simple, and researchers tend to be able to receive and analyze the data quickly.
Reduced testing fatigue
Since each participant is only tested in one condition, between-subjects designs can help avoid testing fatigue that might occur in within-subjects designs where participants go through multiple conditions in one session.
Limitations
A large participant pool is necessary
Because each subject is assigned to only one condition, this type of design requires a large sample. Thus, these studies also require more resources and budgeting to recruit participants and administer the experiments.
Individual differences
Differences between subjects within a given condition may be an explanation for results, introducing error and making the effects of an experimental condition less accurate.
Requires careful matching or random assignment
To help control for individual differences between groups, researchers must carefully match participants on key characteristics or use random assignment to conditions.
If groups differ from the outset, it can confound the results.
Less statistical power
For the same sample size, between-subjects designs have less statistical power than within-subjects designs.
This means larger effect sizes are needed to detect significant differences between conditions, or larger sample sizes are required.
Frequently Asked Questions
What’s the difference between a within-subjects versus a between-subjects design?
Between-subjects and within-subjects designs are two different methods for researchers to assign test participants to different treatments.
Researchers will assign each subject to only one treatment condition in a between-subjects design. In contrast, in a within-subjects design, researchers will test the same participants repeatedly across all conditions.
Between-subjects and within-subjects designs can be used in place of each other or in conjunction with each other.
Each type of experimental design has its own advantages and disadvantages, and it is usually up to the researchers to determine which method will be more beneficial for their study.
Can you use a between-subjects and within-subjects design in the same study?
Yes. Between-subject and within-subject designs can be combined in a single study when you have two or more independent variables (a factorial design).
Factorial designs are a type of experiment where multiple independent variables are tested. Each level of one independent variable (a factor) is combined with each level of every other independent variable to produce different conditions.
Each combination becomes a condition in the experiment. In a factorial experiment, the researcher has to decide for each independent variable whether to use a between-subjects design or a within-subjects design.
In a mixed factorial design, researchers will manipulate one independent variable between subjects and another within subjects.
What is between subject factorial design?
A between-subject factorial design is an experimental setup where participants are randomly assigned to different levels of two or more independent variables.
This design allows researchers to examine the individual effects of each independent variable and their interaction effect on the dependent variable, while each participant is exposed to only one combination of conditions.
References
Allen, M. (2017). The sage encyclopedia of communication research methods (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411
Baeyens, F., Díaz, E., & Ruiz, G. (2005). Resistance to extinction of human evaluative conditioning using a between‐subjects design. Cognition & Emotion, 19(2), 245-268.
Birnbaum, M. H. (1999). How to show that 9> 221: Collect judgments in a between-subjects design. Psychological Methods, 4(3), 243.
Carey, A. A., Lester, T. G., & Valencia, R. M. (2016). The Effects of a Fatal Vision Goggles Intervention on Middle School Aged Children’s Attitudes toward Drinking and Driving and Texting and Driving as Related to Impulsivity: A Between Subjects Design (Doctoral dissertation, Brenau University).
Chang, H. I., & Kang, W. C. (2018). The Impact of the 2018 North Korea-United States Summit on South Koreans’ Altruism Toward and Trust in North Korean Refugees: Between-Subjects Design Around the Summit. Available at SSRN 3270334.
Egele, V. S., Kiefer, L. H., & Stark, R. (2021). Faking self-reports of health behavior: a comparison between a within-and a between-subjects design. Health psychology and behavioral medicine, 9(1), 895-916.
Ehrlichman, H., Brown Kuhl, S., Zhu, J., & WRRENBURG, S. (1997). Startle reflex modulation by pleasant and unpleasant odors in a between‐subjects design. Psychophysiology, 34(6), 726-729.
Jhangiani, R. S., Chiang, I.-C. A., Cuttler, C., & Leighton, D. C. (2019, August 1). Experimental Design. Research Methods in Psychology. Retrieved from https://kpu.pressbooks.pub/psychmethods4e/
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