repeatedmeasure

Repeated measure refers to data collected from the same experimental unit, such as a person or object, at multiple times or under multiple conditions. In a repeated-measures design, each subject experiences all levels of the independent variable or is measured across successive time points. This approach contrasts with a between-subjects design, where different subjects are assigned to each condition.

The main advantage of repeated measures is reduced between-subject variability, which can increase statistical power and allow smaller sample sizes. It is commonly used in psychology, medicine, and social sciences to study development, learning, treatment effects, or time-related change. However, it is susceptible to carryover or order effects, where responses in one condition influence responses in subsequent conditions. Counterbalancing, randomization of condition order, and washout periods are often employed to mitigate these effects.

Statistical analysis of repeated measures often begins with repeated-measures analysis of variance (ANOVA), which tests for differences across time points or conditions while accounting for within-subject correlation. When the sphericity assumption is violated, corrections such as Greenhouse-Geisser or Huynh-Feldt are used. More flexible approaches include linear mixed-effects models and generalized estimating equations, which handle missing data, unbalanced designs, and complex time structures.

Common pitfalls include missing data, improper handling of correlations, and overinterpretation of small effects in small samples. Proper study design and appropriate analytic methods are essential to draw valid conclusions from repeated-measures data.