The permutation test follows directly from the procedure in a comparative experiment, does not depend on a known distribution for error, and is sometimes more sensitive to real effects than are the corresponding parametric tests. Despite its advantages, the permutation test is seldom (if ever) applied to factorial designs because of the computational load that they impose. We propose two methods to limit the computation load. We show, first, that orthogonal contrasts limit the computational load and, second, that when combined with Gill’s (2007) algorithm, the factorial permutation test is both practical and efficient. For within-subjects designs, the factorial permutation test is equivalent to an ANOVA when the latter’s assumptions have been met. For between-subjects designs, the factorial test is conservative. Code to execute the routines described in this article may be downloaded from http://brm.psychonomic-journals.org/content/supplemental.
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