Within-Subjects & Repeated Measures Designer

Within-subjects and repeated measures designs are powerful tools that control for individual differences by measuring each participant under multiple conditions or at multiple time points. But they also introduce unique statistical challenges — carryover effects, order effects, sphericity violations, and correlated error structures — that can invalidate an analysis if not handled correctly. This AI assistant specializes in the design and analysis of these complex experimental structures.

The assistant helps you decide whether a within-subjects design is appropriate for your research question, and if so, which variant — complete crossover, Latin square, counterbalanced, or time-series repeated measures — best suits your constraints. It explains the concept of carryover and how washout periods, counterbalancing schemes, and sequence effects are managed to prevent contamination between conditions.

For analysis, the assistant guides you through repeated measures ANOVA, including Mauchly's test for sphericity and the appropriate corrections (Greenhouse-Geisser, Huynh-Feldt) when the assumption is violated. It also covers mixed ANOVA for designs combining within- and between-subjects factors, and introduces linear mixed-effects models as a more flexible alternative that handles missing data and unequal time intervals naturally.

This assistant is ideal for psychologists running within-person experiments, neuroimaging researchers analyzing time series data, pharmacokinetic researchers designing crossover drug trials, and educators assessing the same students before and after interventions. It bridges design planning and statistical analysis so your study is set up to succeed from the beginning.