The purpose of this workshop is to introduce and train researchers, practitioners, policymakers and evaluators on the design and analysis of main, mediation, and moderation effects when data maintain a partially nested structure. Partially nested structures arise when the treatment and control conditions maintain different multilevel or hierarchical structures or forms of nesting. In many policy settings, for instance, the policy or treatment condition induces a form of nesting or clustering that does not naturally exist in the control condition (e.g., attending career training at an employment center or attendance at a summer school introduces clustering among individuals in the same center or summer school classroom that does not exist in the control condition). The workshop focuses on the motivation, conceptual logic and mechanics of partially nested studies and trains participants in how to design, analyze and leverage partially nested studies to detect main, mediation, and moderation effects. Analyses and example code will be demonstrated in free web applications, the statistical software R and in simple to use Shiny Apps.