Monte Carlo simulations play a critical role in furthering our understanding of results from structural equation modeling (SEM). Among the factors manipulated in SEM simulation studies, model misspecification is considered important and deserves attention from researchers. Three approaches have been suggested and used in quantifying severity of specification errors in SEM simulations, including the number of parameters in a true model omitted, power of a statistical test in rejecting a misspecified model, and the value of root mean square error of approximation (RMSEA) of a misspecified model. The present study illustrates the relations among these three approaches for quantifying degrees of model misspecification in SEM with a graphical representation. Graphs of power and RMSEA for models omitting one to three parameters were plotted against different numbers of variables and parameter values using three types of models. The relations among these three methods were shown to depend on model size, parameter values, and model type. Researchers interested in manipulating severity of model misspecification are encouraged to examine the relations among the quantities expressed by these three approaches to select an optimal method for quantifying specification errors for the research issue to be pursued.