The traditional univariate ANOVA for the repeated ~neasures or split-plol, design, commonly used in the behavioral sciences, requires, in addition to the usual assumptions of error normality and variance homogeneity, that the covariance matrix for the repeated measures have a special form (Type H). Because detection of lack of co~npliance with these assumptions is problematic, this design is a good candidate for alternative analysis. This paper illustrates an application of Efron's bootstrap to the repeated measures design. While the bootstrap approach does not require parametric assumptions, it does utilize distributional information in the sample. By appropriately resampling from the data collected in a study, the bootstrap may determine quite accurate sampling distributions for estimators, effects, or contrasts of interest.
Mendeley saves you time finding and organizing research
Choose a citation style from the tabs below