Repeated measures, interventions, and time series analysis.

Abstract

Classical repeated measures designs assume treatments are given in a randomized order. When randomization is not performed and an experiment involves a sequence of observations on each subject collected over time, serial correlations may become important. An example of these types of data is an intervention experiment wherein subjects are observed before and after a treatment or other manipulation. This situation falls within the realm of time series analysis. The correlations between observations often depend on the time intervals between the observations; observations that are closely spaced in time usually are more highly correlated than those with a larger time separation. This report demonstrates a test for such serial correlation and discusses a method of adjusting for it in repeated measures experiments.