In experimental design, combining blocking factors with repeated measures provides a powerful framework for reducing error variance. While a standard randomized complete block design (RCBD) isolates nuisance variation by grouping homogeneous experimental units into blocks, incorporating repeated measures tracks these same units across multiple time points or sequential treatment conditions.
This structural intersection changes the error covariance requirements of your statistical model. Unlike completely randomized designs where observations are independent, repeated measures within a block introduce multiple layers of correlation: correlation among different units within the same block, and correlation across repeated time points within the same individual unit.
To analyze this design accurately, researchers typically deploy either a mixed-effects baseline configuration or a multivariate approach (MANOVA). When using standard univariate split-plot models, satisfying the sphericity assumption (equality of variances of the differences between all possible pairs of repeated groups) is critical. If sphericity is violated, adjustment factors like the Greenhouse-Geisser or Huynh-Feldt corrections must be applied to modify the degrees of freedom and protect against inflated Type I error rates.