What is the difference between within participants and between participants

You can conduct usability testing studies using either the between-subjects study approach or the within-subjects approach depending upon your goals and resources. Between-subjects studies or within-subjects studies — which approach do you prefer for user research?

Share your thoughts in the comments section below. The modern guide to web accessibility Guide. User Experience 6 minute read by, Rich Staats. There are two possible ways of doing it: Between-subjects study experiment: This study design involves assigning different user interface to different test participant.

This way, each test participant interacts with one user interface. This way, each test participant will test all of the conditions. For example, when comparing the user interfaces of two e-commerce websites X and Y to see how easy it is for users to use filters and add products to their shopping cart, you could go about it one of two ways: Between-subjects study experiment: You assign each test participant a different website.

Within-subjects study experiment: Each test participant is instructed to complete the task on both websites X and Y. Experimental design in quantitative studies In the context of usability testing, the primary goal of quantitative studies can be to compare: Different iterations of the same user interface.

Summary: In user research, between-groups designs reduce learning effects; repeated-measures designs require fewer participants and minimize the random noise. By Raluca Budiu. When you want to compare several user interfaces in a single study, there are two ways of assigning your test participants to these multiple conditions:. For example, if we wanted to compare two car-rental sites A and B by looking at how participants book cars on each site, our study could be designed in two different ways, both perfectly legitimate:.

Any type of user research that involves more than a single test condition has to determine whether to be between-subjects or within-subjects. However, the distinction is particularly important for quantitative studies. Unlike qualitative studies, quantitative usability studies aim to result in findings that are statistically likely to generalize to the whole user population.

Often, the main goal of quantitative usability studies is to compare — a site with its competitors, two different iterations of a design, or two different groups of users such as experts vs. Like in any scientific experiment in which we want to detect causal relationships, a quantitative study involves two types of variables :. If the study produces statistically significant results, then we can say that a change in the independent variable caused a change in the dependent variable.

If we wanted to measure which of the two sites, A or B, is better for the task of reserving a car, we could choose Site with two possible values or levels — A and B as the independent variable, and the time on task and the accuracy for booking a car could be the dependent variables. The goal of the study would be to see whether the dependent variables time and accuracy change when we vary the site or they stay the same.

If they stay the same, then none of the sites is better than the other. To plan our study, the next question that we need to answer is whether the study design should be between-subjects or within-subjects — that is, whether a participant in the study should be exposed to all the different conditions for the independent variable in our study within-subjects or only to one condition between-subjects.

See an example. Within-subjects designs help you detect causal or correlational relationships between variables with relatively small samples. 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. 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. Carryover effects are a broad category of internal validity threats that occur when an earlier treatment alters the outcomes of a later treatment. In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions. Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables 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. Have a language expert improve your writing. Check your paper for plagiarism in 10 minutes. Do the check. Generate your APA citations for free! While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power compared to a within-subjects design.

Carryover effects are the lingering effects of being in one experimental condition on a subsequent condition in within-subjects designs. Between-subjects designs also prevent fatigue effects , which occur when participants become tired or bored of multiple treatments in a row in within-subjects designs. Carryover effects threaten the internal validity of a study. In a between-subjects design, each participant is only given one treatment, so every session can be fairly quick. In contrast, data collection in a within-subjects design takes longer because every participant is given multiple treatments.

However, despite the data collection duration per participant taking longer, you need fewer participants compared to between-subjects design. Between-subjects designs require more participants for each condition to match the high statistical power of within-subjects designs.

That means that they also require more resources to recruit a larger sample , administer sessions, and cover costs etc. To counter this in a between-subjects design, you can use matching to pair specific individuals or groups in your sample. That way, the groups are matched on specific variables e.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions. In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions. While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design.

Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables 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. Have a language expert improve your writing.