Abstract
Complete randomization for many industrial and agricultural experiments is frequently impractical due to constraints in time, cost, or existence of one or more hard-to-change factors. In these situations, restrictions on randomization may lead to split-plot designs, allowing certain factor levels to be randomly applied to the whole plot units and remaining factor levels randomly applied to the subplot units. Separate random errors in whole and subplot units from the two randomizations results in a compound symmetric error structure, which affects estimation, inference, and choice of design. In this article, we consider the prediction properties of split-plot designs, expanding the comparison between designs beyond parameter estimation properties and present three-dimensional variance dispersion graphs. These graphs can be used for evaluating the prediction capability of split-plot designs as well as for developing design strategies. We demonstrate, through several examples, that three-dimensional variance dispersion plots offer a more comprehensive study of competing designs than what is offered when comparing designs with single number optimality criteria (such as D-, G-, and V-criteria).