Title I evaluations using the RMC Model C design depend for their interpretation on the assumption that the regression of posttest on pretest is linear across the cut score level when there is no treatment; but there are many instances where nonlinearities may occur. If one applies the analysis of covariance, or model C analysis, large errors may result. Various methods are suggested for model C users to deal with non-linearities. One method is Mosteller and Tukey's re-expression of posttest scores. Another method depends upon differential weighting of scores in different parts of the pretest distribution. A third method uses quadratic regression lines and extrapolation to obtain a "no-treatment expectation." Still another method is to assume parallel regression lines for the treatment and control groups. A number of different logistic curves may be tried if both floor and ceiling effects are present. Graphing the scatterplot of pretest versus posttest scores is also recommended. (Author/CTM)