A large number of pairwise multiple comparisons (P-MCPs) have been introduced recently to the educational research community. The use of these P-MCPs with single group repeated measures data was studied through an exploratory Monte Carlo study of P-MCPs that have been shown to control different types of Type 2 error and Type 1 familywise error under both no violations and violations of assumptions in other designs. A second purpose of the study was to recommend the P-MCPs based on ease of use. The stringent level of robustness developed by J. V. Bradley (1978) was used to examine the P-MCPs empirical rate of Type 1 error, and the range of sphericity was expanded to cover the values found in practice more realistically. Pairwise power among the P-MCPs was also compared. Nine P-MCPs were studied. Results indicate that all the new methods can not be recommended with single group repeated measures designs because their omnibus tests failed to control Type I error adequately. A familiar and easy-to-calculate method, the Dunn-Bonferroni procedure, successfully controlled familywise Type I error and may be recommended for use as a followup procedure with single group repeated measures designs. Further research with single group repeated measures designs through the Studentized maximum modulus statistic is recommended. (Contains 3 tables and 27 references.) (SLD)