Generating meta-inferences for program improvement recommendations: mixed methods integration in joint displays for program evaluation.

Integration is a defining feature of mixed methods research, and the generation of meta-inferences is the overarching outcome of a mixed methods study. Despite their role in demonstrating the added value of mixed methods research, meta-inferences are frequently limited to determining fit between the results, through a comparison of quantitative and qualitative results. This article builds on a small but growing literature that has expanded conceptualization of meta-inferences by proposing an additional type that generates program recommendations. We advocate enlarging this framework to include other types of meta-inferences to give researchers additional options for addressing research questions. Thus, the aim of this paper is to demonstrate an underutilized type of meta-inference that generates program implications and recommendations. Because the goal of evaluation is the utilization of results to make program decisions, this example provides an opportunity to showcase the value of meta-inferences as applied, real world, actionable insights. We provide three examples of generating meta-inferences that focus on identifying implications and program recommendations using joint displays from an ongoing evaluation of a prevention program.