11.1 - Historical Methods

Repeated measures in time have historically been handled as either a multivariate analysis or as a univariate split-plot in time. The focus in this course is limited to only the latter.

A split-plot in time approach looks at each subject (experimental unit) as a main plot which is then split into sub-plots (time periods). Historically, the default assumption in split-plot in time data analyses has been that the correlations among responses at different time points are the same for all treatment levels (compound symmetry). However, depending on the study and nature of data, other correlation structures can be more appropriate (e.g. autoregressive lag 1).

Most of the current software facilitates the inclusion of different correlation structures which now allows for repeated measures to accommodate the presence of different correlated structures in residuals.