split-plot design
a variation of a full factorial design in which one of the independent variables is held constant while all other combinations of conditions are examined, often using different sample sizes or different randomization schemes. For example, consider a researcher examining the influence on crop yield of four different types of corn seed, three different types of fertilizer, and two different types of planting technique. He or she could have half of the participating farmers plant all of the seed types using one technique and the other half plant all of the seed types using the second technique. Split-plot designs are particularly common in agricultural and industrial contexts, in which certain conditions may be difficult to manipulate or change for experimental purposes. Data from such designs may be examined with a split-plot analysis of variance.