Ihre Browserversion ist veraltet. Wir empfehlen, Ihren Browser auf die neueste Version zu aktualisieren.

Welcome on my website!

I am a post-doctoral research associate at the University of Geneva, where my primary work focuses on the development and application of empirical research and evaluation methods, including quantitative, qualitative and mixed-approach techniques, data analytics and questions of causal inference. In this connection, I concentrate on configurational comparative methods (CCMs), such as Coincidence Analysis (CNA), Event Structure Analysis (ESA) and Qualitative Comparative Analysis (QCA), in particular. I also have a strong interest in questions of research design more broadly defined as well as meta-science, i.e. the scientific study of science itself. Last but not least, I occupy myself with possibilities to improve the teaching of empirical research methods and data analytics at all levels of higher education, using computational innovations and graphical tools.

Professional profiles of my work can be found on the following services:  



You find seven further pages on this site. About provides a short CV. Under News, I post information on my latest publications and events. Commentaries on various topics of interest to me are collected under Blog. Software I have authored can be found under SoftwareTeaching offers material from courses I have taught over the years. My electronic business card can be found under Contact, and Misc is just a collection of links that might be useful to you if you happen to share some of my research interests or teaching activities.

Latest News: Article on methodological evaluation of QCA accepted at Sociological Methods & Research 

To date, hundreds of researchers have employed the method of Qualitative Comparative Analysis (QCA) for the purpose of causal inference. In a series of simulation studies, several authors have recently questioned the correctness of QCA in this connection. These previous attempts at evaluating QCA, however, have been defective. We lay out a set of formal criteria for an adequate evaluation before implementing a battery of inverse-search trials to test how QCA performs in different recovery contexts. Our results indicate that QCA is correct when generating the parsimonious solution type, but incorrect in conjunction with the conservative and the intermediate solution type.