The Multi-Stage Mixed Methods Framework
A new research design to combine hypothesis development and hypothesis testing and to embed machine learning and practitioner engagement in the social sciences
In this open-access article my co-authors Giuditta Fontana, Argyro Kartsonaki, Natascha S. Neudorfer and I argue that Multi-methods research designs typically focus either on developing or on testing pre-existing hypotheses. We outline a new methodological framework to combine hypothesis development and testing into a coherent and robust multi-method research design: the Multi-Stage Mixed-Methods Framework (MSMMF). The MSMMF is a novel approach to carefully sequence and combine different methods, such as machine learning (ML), practitioner engagement, inferential statistical analysis, qualitative comparative analysis, process-tracing and/or congruence analysis. We demonstrate that the MSMMF provides a holistic research design for developing and testing hypotheses, combining the strengths of existing mixed-methods approaches and embedding ML and the involvement of practitioners throughout the research process. We present the MSMMF’s application to a theoretically challenging, empirically rich and policy-relevant question: Why do some peace processes bring an end to large-scale conflict-related violence while others do not?
This is the question that we explore more fully in our co-authored book How to Prevent Civil War Recurrence: Learning from Failure.
