Published on March 29, 2021 by Pritha Bhandari. Revised on July 21, 2022. In experiments, a different
independent variable treatment or manipulation is used in each condition to assess whether there is a cause-and-effect relationship with a dependent variable. In a within-subjects design, or a within-groups design, all participants take part in every condition. It’s the opposite of a
between-subjects design, where each participant experiences only one condition. A within-subjects design is also called a dependent groups or repeated measures design because researchers compare related measures from the same participants between different conditions. All
longitudinal studies use within-subjects designs to assess changes within the same individuals over time. 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. Each story uses a different tone and
style. After each story, participants are asked how they feel about it and their willingness to donate to a related cause. Other unrelated questions are also asked to make sure participants don’t guess the aim of the study. To test the effects of messaging styles on generosity, you compare the willingness to donate across conditions within subjects. When comparing different treatments within subjects, you should randomize or counterbalance
the order in which every condition is presented across the group of participants. This prevents the effects of earlier treatments from spilling over onto later ones. Randomization means using many different possible sequences for treatments, while counterbalancing means using a limited number of sequences across the group. Counterbalancing is sometimes more convenient for researchers because an even portion of the sample undergoes each sequence of conditions selected by
researchers. Each treatment ideally appears equally often in each position (e.g., third) of the sequence. This helps balance out the effects of treatment sequence on the outcomes. To counterbalance treatment order, you first decide on a few fixed sequences for these treatments: A-B-C-D-E, B-E-A-C-D, and D-A-B-E-C, etc. Then you divide your participants into sub-groups that each receive only one sequence of treatments. To randomize treatment order, the order of the short stories is completely randomized between participants using a computer program. Every possible sequence can be presented to participants across the group, but in complete randomization, you can’t control how often each sequence is used in the participant group. In longitudinal studies, time is an independent variable. Because researchers can’t prevent the effects of time, longitudinal studies usually study correlations between time and other (dependent) variables. Example: Within-subjects design over timeAs a social researcher, you’re studying the effects of time (the independent variable) on perceptions of the pandemic and coronavirus (the dependent variable). You gathered a large sample of participants early in 2020 and have repeatedly sent them an online survey every two to three months since then.An important question in the Likert scale survey asks participants to rate their fear of getting COVID-19 on a scale from 1–7. To assess changes in perception, you compare differences in survey responses over time within subjects. Within-subjects versus between-subjects designThe opposite of a within-subjects design is a between-subjects design, where each participant only experiences one condition, and different treatment groups are compared. Between-subjects designs usually have a control group (e.g., no treatment) and an experimental group, or multiple groups that differ on a variable (e.g., gender, ethnicity, test score etc). Researchers compare the outcomes of different groups with each other. In within-subjects designs, participants serve as their own control by providing baseline scores across different conditions. The word “within” means you’re comparing different conditions within the same group or individual, while the word “between” means that you’re comparing different conditions between groups.  If you use a between-subjects design, you would split your sample into two groups of participants:
You would administer the same test to all participants and compare test scores between the groups. If you use a within-subjects design, everyone in your sample would take part in every condition:
You would randomize the order of the learning environment across the participants: some participants would first take the course on campus before switching to online learning, while the others would take the course online first before taking it in person. Then, you compare test scores within subjects between the two conditions. In factorial designs, two or more independent variables are tested at the same time. Every level of one independent variable is combined with each level of every other independent variable to create different conditions. In a mixed factorial design, one variable is altered between subjects and another is altered within subjects. Some longitudinal studies can be experimental when they use a mixed design to study two or more independent variables. If you can directly manipulate one of the independent variables, and participant assignment to conditions, you’re using an experimental approach. Example: Experimental longitudinal studyAs an education researcher, you’re investigating whether different teaching methods impact second language learning outcomes. Your participants are students in the 8th grade taking an introductory course of a second language.In a mixed design, you experimentally vary teaching methods between subjects, and you measure language knowledge at multiple time points within subjects. Each participant is randomly assigned to one of two groups:
All participants are tested before, midway and after taking the course, and their scores are statistically tested for differences across time and between groups. What can proofreading do for your paper?Scribbr editors not only correct grammar and spelling mistakes, but also strengthen your writing by making sure your paper is free of vague language, redundant words and awkward phrasing. See editing example Pros and cons of a within-subjects design
Within-subjects designs help you detect causal or correlational relationships between variables with relatively small samples. It’s easier to recruit a sample for a within-subjects design than a between-subjects design because you need fewer participants. Every participant provides repeated measures, making the study more cost effective.
In a between-subjects design, different participants take part in each condition, so participant characteristics (e.g., intelligence or memory capacity) often vary between groups. This means it’s hard to say whether the outcomes are truly the result of the independent variable or individual differences between groups. In contrast, there are no variations in individual differences between conditions in a within-subjects design because the same individuals participate in all conditions. Participant characteristics are controlled for.
A within-subjects design is more statistically powerful than a between-subjects design, because individual variation is removed. To achieve the same level of power, a between-subjects design often requires double the number of participants (or more) that a within-subjects design does.
There are many time-related threats to internal validity that only apply to within-subjects design because it’s hard to control the effects of time on the outcomes of the study. Some examples:
Carryover effects are a broad category of internal validity threats that occur when an earlier treatment alters the outcomes of a later treatment. Some examples:
Randomization and counterbalancing of the order of conditions can help reduce carryover effects. Frequently asked questions about within-subjects designsWhat is a factorial design? In a factorial design, multiple independent variables are tested. If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions. Cite this Scribbr articleIf you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
Is this article helpful?You have already voted. Thanks :-) Your vote is saved :-) Processing your vote... What does it mean to counterbalance an experiment?Counterbalancing is a procedure that allows a researcher to control the effects of nuisance variables in designs where the same participants are repeatedly subjected to conditions, treatments, or stimuli (e.g., within-subjects or repeated-measures designs).
What does counterbalancing protect against in within groups designs?Counterbalancing removes confounding variables from an experiment by giving slightly different treatments to different participant groups.
Why is it important to counterbalance the order in which conditions are presented to participants?Random assignment to conditions in between-subjects experiments or counterbalancing of orders of conditions in within-subjects experiments is a fundamental element of experimental research. The purpose of these techniques is to control extraneous variables so that they do not become confounding variables.
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