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Missing data are a common problem in educational research. A promising technique, that can be implemented in SAS PROC MIXED and is therefore widely available, is to use maximum likelihood to estimate model parameters and base hypothesis tests on these estimates. However, it is not clear which test statistic in PROC MIXED performs better with missing data. The performance of the Hotelling-Lawley-McKeon and Kenward-Roger omnibus test statistics on the means for a single factor within-subject ANOVA are compared. The results indicate that the Kenward-Roger statistic performed better in terms of keeping the Type I error close to the nominal alpha level.

Type I Error Rates For A One Factor Within-Subjects Design With Missing Values