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

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). MSMMF is a novel approach to carefully sequence and combine different methods, including machine learning, practitioner engagement, inferential statistical analysis, qualitative comparative analysis, process-tracing, and/or congruence analysis. We demonstrate that MSMMF provides a holistic research design for developing and testing hypotheses, combining the strengths of existing mixed-methods approaches and embedding machine learning and the involvement of practitioners throughout the research process. We present 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?