*Names in bold indicate Presenter
However, the sheer presence of a CRT to evaluate a program or policy is not enough to generate rigorous evidence of the effectiveness of a program. As noted above, the trials must be well-designed and implemented in order to generate high-quality evidence of program effectiveness. In the past 15 years, the field has made substantial progress in terms of how to design CRTs and how to calculate the statistical power for the main effect of treatment. However, designing a study to detect the main effect of treatment may not be sufficient. It is quite reasonable that context matters in these studies and thus designing studies to examine for whom and under what conditions a program is effective is critical.
The purpose of this paper is to provide a resource for researchers designing studies that not only test whether or not a program works, but under what conditions and for whom. Specifically, I provide power calculations for dichotomous student and cluster level moderators for the following types of CRTs: 2-level CRT, 3-level CRT, 3-level multisite cluster randomized trial (MSCRT), and 4-level MSCRT. To make the calculations accessible for researchers planning CRTs, I also include a tool, R code in this case, for researchers conducting these calculations. The calculations and code in this paper is a next step towards building capacity in field for researchers to design CRTs that move beyond the main effect of treatment.