The Comparative Short Interrupted Time Series (C-SITS) design is a frequently employed quasi-experimental method, in which the pre- and post-intervention changes observed in the outcome levels of a treatment group is compared with those of a comparison group where the difference between the former and the latter is attributed to the treatment. The increase in the availability and quality of extant data (e.g., state test scores, graduation rates, and college application rates in primary and secondary education and cognitive, language, and socio-emotional assessments in pre-school settings) has made the use of C-SITS designs a more viable option for assessing the impacts of interventions. Despite the recent growth in its use, the existing resources on how to estimate minimum detectable effects for this design are still very limited. This paper aims to address the limitations of existing research on this topic by: (1) deriving expressions for the variance of the various C-SITS estimators; and (2) providing plausible values for the critical variance parameters calculated using school-level test scores from state assessments. The study is ongoing at the time of this publication. Preliminary findings show that when appropriate plausible values are entered into the formulae provided in this paper, there is a close correspondence between the standard errors estimated from the formulae and those obtained from fitting models to actual data. The formulae and plausible values in this paper provide analysts with a flexible basis for estimating MDES for various C-SITS designs, and serves as a template for accumulation of relevant information that can be used to build databases of plausible values that inform the designs of future studies. Tables and figures are appended.