The basic idea of mixed- or multi-method approaches is to overcome weaknesses of one method with the strength of another. However, thus far most of the literature has concentrated on how to combine (or “mix”) quantitative and qualitative methods. In our view, this is too narrow and has resulted in RCTs being complemented by a few focus group discussions or the implementation of a survey as part of a qualitative case study. While this is certainly meaningful for the individual studies, the overarching aim is to achieve more comprehensive evaluation designs that improve the measurement of causal inference and answer complex and multi-dimensional evaluation questions. In this sense, the authors regard comprehensive multi-method approaches as going beyond combining qualitative and quantitative data. Rather, they combine theorising about how activities lead to outcome and impacts (as in theories of change) with different types of causal inference (cf. Goertz, 2017).

For instance, RCTs or quasi-experiments rely on the counterfactual logic, comparing the outcome of one or more treatment groups to the outcome of controls, in other words comparing the beneficiaries of an intervention with those not having received the intervention.