Within vs. between-subjects in UX research design

Research is a crucial component of UX design, but limited resources often necessitate strategic study designs. Within-subjects and between-subjects designs are two key methodologies that influence how UX findings are interpreted. Within-subjects design involves the same participants experiencing multiple design variations, allowing for comparison against their own baseline, though it risks carryover effects that can be mitigated through counterbalancing. Conversely, between-subjects design assigns different groups to individual variations, mimicking real-world usage but requiring larger sample sizes due to individual differences. The choice between these methods hinges on factors such as research goals, resource availability, and potential biases. Statistical tools like t-tests and ANOVA are essential for analyzing UX data, with paired t-tests and repeated measures ANOVA suited for within-subjects designs, while independent t-tests and one-way ANOVA are apt for between-subjects designs. These choices are pivotal in translating raw data into impactful UX insights.