Some Approximate Tests for Repeated Measurement Designs

Abstract

Four approximate tests are considered for repeated measurement designs in which observations are multivariate normal with arbitrary covariance matrices. In these tests traditional within-subject mean square ratios are compared with critical values derived from F distributions with adjusted degrees of freedom. Two of them—the ∈ approximate and the improved general approximate (IGA) tests-behave adequately in terms of Type I error. Generally, the IGA test functions better than the ∈ approximate test, however the latter involves less computations. In regards to power, the IGA test may compete with one multivariate procedure when the assumptions of the latter are tenable.