The data analysis problem posed by a repeated measures design that includes a single observation on a covariate for each subject is considered. The current paper discusses how best to capture a possible dependence of the effect of the within-subject factor on the level of the covariate. Procedures originally explicated by Rogosa (1980) for dealing with heterogeneity of regression in the between-subjects case are extended to this repeated measures situation. We conclude that such pick-a-point and simultaneous inferential procedures not only provide more powerful overall tests of the within-subject effects but also permit a thorough analysis of the attribute-by-treatment interaction implied by a significant regression of the within effect on the covariate. (Author)