Understanding the types of study designs is crucial for gathering meaningful insights and data. Three common approaches are within-subject, between-subject, and mixed studies. But before we delve into these study types, let's first clarify what "factors" mean in the research context.

What Are the Factors?

In research, a factor is a variable or condition that researchers manipulate or observe to study its effect on the outcome of interest. For example, if you're conducting UX research on a new e-commerce website, factors might include the design layout or color scheme.

Understanding factors is crucial because they form the basis for designing different types of UX studies. With this foundational knowledge in mind, let's explore three common study types: within-subject, between-subject, and mixed studies, and understand why they matter in UX research.

Within-Subject Studies

Within-subject studies, also known as repeated measures studies, involve testing the same group of participants under different conditions or treatments, a set of tasks for example (A, B, C).

This approach allows researchers to measure how individuals respond to changes within themselves. Imagine you are designing a new mobile app interface, and you want to understand how users adapt to different navigation layouts.

Importance of Counterbalancing:

Counterbalancing is a critical aspect of within-subject studies. It involves systematically changing the order in which participants experience the conditions (tasks) to minimize the impact of order effects.

Without counterbalancing, the sequence in which participants encounter conditions (tasks) might influence their responses, potentially leading to skewed results.

You can read more about counterbalancing and carry-over effects in my other blog post.

Between-Subject Studies

In contrast to within-subject studies, between-subject studies involve testing different groups of participants under various conditions. For instance, if you want to compare three different designs (A, B, C) of a website's homepage, you might assign each design to a different separate group of users.

In a between-subjects design study, the focus is primarily on different groups of participants rather than specific tasks. This type of experimental design is used to compare the effects of different treatments or conditions by assigning different groups of participants to each treatment condition. Each group of participants experiences only one of the conditions, and their responses or outcomes are compared to draw conclusions.

Importance of Larger Sample Size:

One key consideration in between-subject studies is the need for a larger sample size. This is because between-subject designs inherently introduce more variability into the study. Each group of participants is a unique entity, and individual differences among participants can significantly impact the results.

A larger sample size helps mitigate the effects of this variability, increasing the study's statistical power and the likelihood of detecting meaningful differences between conditions. In UX research, this means you're more likely to identify design preferences or performance variations that are statistically significant and not just the result of random chance.

Third Approach: Mixed Studies

Mixed studies, as the name suggests, combine elements of both within-subject and between-subject designs. They aim to capture the advantages of each approach while minimizing their drawbacks.

In a mixed study, you might have some participants who experience multiple conditions (within-subject) while others only experience a single condition (between-subject).

This design is useful when you want to investigate both general trends and individual differences. For example, you might be interested in how users in general respond to a new feature (between-subject), but also how specific users adapt to it over time (within-subject).

Summary

The choice between within-subject, between-subject, or mixed studies in UX research depends on your research goals and the specific questions you're trying to answer. Each approach has its strengths and weaknesses, but understanding these can help you design more effective and informative studies.

Remember, in within-subject studies, counterbalancing ensures fair testing, while in between-subject studies, a larger sample size is essential to account for greater variance across groups. Mixed studies offer a flexible middle ground when you need to balance these considerations.