Qualitative Comparative Analysis: A Mixed-Method Tool for Complex Implementation Questions

The translation and scale-up of evidence-based programs require new methods to guide implementation decisions across varying contexts. As programs are translated to real-world settings, variability is introduced. Some program components may have minor roles to play in producing positive outcomes, and some may have major roles, but only if adapted to meet different contextual demands. While some sources of variability are likely to improve program outcomes, we currently lack methods that allow us to determine the critical components or combinations of components that serve as causal pathways to a desired outcome and then to advise practitioners accordingly. In this paper, we introduce a promising tool for this purpose and illustrate its use in a translational research context. Qualitative Comparative Analysis (QCA) is often used to examine causality in situations that have complex, multiply-determined outcomes. The basic premise of QCA is that different sets of causal conditions, or causal pathways, may lead to a single outcome (the principle of equifinality). We applied QCA to a selection of the highest- and lowest-performing programs from a multi-year two-state dissemination of The Strengthening Families Program for Parents and Adolescents 10–14 to determine which components or combinations of components at the implementation, program delivery, and participant levels produced desired participant outcomes. In particular, we examined which components were necessary (i.e., in the absence of these factors, the outcome didnot occur), and which were sufficient (i.e., in the presence of these factors, the outcome always occurred). Results demonstrated that certain conditions were necessary for program success. In addition, given those necessary conditions, there were two sets of conditions sufficient to produce success, regardless of the presence or absence of any of the others. QCA, not previously used in prevention science research, helps to illuminate causal pathways, leading to concrete, evidence-based implementation decisions that facilitate generalization and scale-up.