The Welch-James (WJ) and the Huynh Improved General Approximation (IGA) tests for interaction were examined with respect to Type I error in a between- by within-subjects repeated measures design when data were non-normal, non-spherical and heterogeneous, particularly when group sizes were unequal. The tests were computed with aligned ranks and compared to the use of least squares and robust estimators (i.e., trimmed means and Winsorized variances/covariances). Critical values were either obtained theoretically or through a bootstrapping method. The IGA and WJ procedures based on aligned ranks always provided a valid test of a repeated measures interaction effect when group sizes were equal and covariance matrices across groups were homogeneous. On the other hand, the use of aligned ranks did not provide a valid test for a repeated measures interaction when covariance matrices were nonspherical with unequal variances across the levels of the repeated measures factor combined with unequal covariance matrices across the grouping factor. The IGA and WJ procedures based on robust estimators provided a valid test of the interaction across investigated conditions, however under a heavy-tailed distribution, the IGA and WJ procedures based on least squares estimators showed better Type I error control than when based on robust estimators.