Factorial design

In the previous designs the assumption was that there were one or two major sources of variation and that the researcher wanted to separate them from the total variance. There was no allowance or even assumption that some factors might interact with one another. Indeed, researchers are often interested in the simultaneous effects of two or more independent variables. In the antifreeze example, the researcher might want to see if outlets serving higher-income clientele have greater success with the higher prices than those outlets serving lower-income clientele. A factorial design measures the influence of interacting variables on a dependent variable. In our antifreeze example, improved sales are associated with both the income of an outlet's clientele and any change in the outlet display used. Factorial designs enable researchers to measure the separate effects of each variable as it works alone. The actual design used can be a randomised block or Latin square, but a more complicated form of variance analysis is applied to the results.

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