Intervention research is often time- and resource-intensive, with numerous participants involved over extended periods of time. In order to maximize the value of intervention studies, multiple outcome measures are often included, either to ensure a diverse set of outcomes is being assessed or to refine assessments of specific outcomes. Here, we advocate for combining assessments, rather than relying on individual measures assessed separately, to better evaluate the effectiveness of interventions. Specifically, we argue that by pooling information from individual measures into a single outcome, composite scores can provide finer estimates of the underlying theoretical construct of interest, while retaining important properties more sophisticated methods often forego, such as transparency and interpretability. We describe different methods to compute, evaluate, and use composites, depending on the goals, design, and data. To promote usability, we also provide a preregistration template that includes examples in the context of psychological interventions, with supporting R code. Finally, we make a number of recommendations to help ensure that intervention studies are designed in a way that maximizes discoveries. A Shiny app and detailed R code accompany this paper, and are available at: https://osf.io/u96em/.