From Research Question to Analytical Strategy

Choosing and applying the right methods for rigorous empirical research

Rigorous empirical research demands more than statistical know-how. It requires the judgement to match the right method to the right question, and to defend that choice to reviewers, decision-makers, and peers.

This five-day course is designed for professionals and researchers who already have a statistical foundation but want to sharpen the analytical reasoning behind their empirical work.

Rather than focusing on software commands, this intensive programme takes a theory-driven approach to advanced methods. You will learn to translate research questions into coherent analytical strategies, distinguish description from association and causation, and apply the right techniques to experimental, survey, and archival data.

Throughout the course, the emphasis is on methodological fit, robustness, and interpretation, connecting results to both theoretical insight and practical, managerial relevance.

By bridging the economics, finance and management research traditions, the programme helps you navigate interdisciplinary research with confidence. You will leave with a clear analytical roadmap for your own research projects and a stronger foundation for producing credible, publishable evidence.

Why join this course?

• Reasoning over recipes
  Learn to choose and justify methods, not just apply them, building the analytical judgement that reviewers, colleagues and decision-makers expect, with a focus on methodological fit rather than software procedures.
• Built for interdisciplinary research
  The programme bridges the economics and management traditions, so you can move confidently across causal inference, SEM, and panel-data methods regardless of your field or research paradigm.
• Directly applied to your own work.
  A hands-on research clinic and guided reflection turn concepts into a concrete, defensible analytical roadmap for your own studies, reports, and publications.
• Look beyond the results 
  Learn to critically assess statistical assumptions, robustness and limitations, and interpret your findings in relation to both theoretical insights and practical relevance.