Repeated measures analysis, also known as within-subjects analysis, is a statistical technique used to analyze data collected from the same subjects at multiple time points or under different conditions. This type of analysis is used when the same individuals are measured multiple times, such as in experiments where the same subjects are tested under different conditions or at different time points.
For example, imagine that you are conducting a study to investigate the effectiveness of a new drug on reducing pain. You might measure the pain levels of each participant at baseline (before the drug is administered), and then again after taking the drug for a period of time. In this case, each participant serves as their own control, and the repeated measures are the pain scores collected at the two time points.
There are different methods for analyzing repeated measures data, such as repeated measures ANOVA, mixed-effects models, and growth curve models. The choice of method will depend on the specific research question and the assumptions of the data.
In general, repeated measures analysis allows to account for the correlation between the multiple observations on the same individual, giving more precise estimates of treatment effects and allowing to control for individual differences.