A guide to qualitative attribution methods for evaluation in conservation

Knowledge of what conservation interventions improve biodiversity outcomes, and in which circumstances, is imperative. Experimental and quasi-experimental methods are increasingly used to establish causal inference and build the evidence base on the effectiveness of interventions, but their ability to provide insight into how and under what conditions an intervention should be implemented to improve biodiversity outcomes faces limitations. A suite of attribution methods that leverage qualitative methods for causal inference is available but underutilized in conversation impact evaluation. This article provides a guide to 5 such qualitative attribution methods: contribution analysis, process tracing, realist evaluation, qualitative comparative analysis, and most significant change. It defines and introduces each method and then illustrates how they could be applied through a case study of community conservancies in Namibia. This guide provides examples of how qualitative attribution methods can advance knowledge of what works, in which contexts, and why in biodiversity conservation.

Automated summary

Understanding which conservation interventions work to improve biodiversity outcomes and in what circumstances is crucial. Experimental and quasi-experimental methods are commonly used to establish causal inference, but they have limitations in providing insights into the implementation of interventions for improved biodiversity outcomes. This article introduces 5 qualitative attribution methods that can fill this gap, including contribution analysis, process tracing, realist evaluation, qualitative comparative analysis, and most significant change. Through a case study of community conservancies in Namibia, the article demonstrates how these methods can advance knowledge of effective interventions in biodiversity conservation.