Introduction: Frontiers of Methodological Progress in Qualitative Research

The recent decade has witnessed an unprecedented flurry of methodological reflection on qualitative approaches in Political Science and related disciplines. In the Anglo-Saxon world, much of this methodological reflection has been (framed, at least, as) a reaction to the attempt to establish the epistemology and methodology that underpins quantitative research as the only legitimate one in the Social Sciences (King, Keohane, and Verba 1994). The book Rethinking Social Enquiry: Diverse Tools, Shared Standards, edited by Henry E. Brady and David Collier (2004), contains a broad spectrum of arguments in favor of distinct tools for generating causal inference in small-N studies. At the same time, Alexander George and Andrew Bennett (2005) pointed to the fundamental importance of "causal-process tracing" as a distinct form of inferring causality in case studies. Furthermore, Charles Ragin proposed a configurational/set-theortical alternative to the co-variational/statistical template proposed by King, Keohane and Verba since the end of the 1980s (Ragin 1987), recently summed up in Redesigning Social Inquiry: Fuzzy Sets and beyond (Ragin 2008). As a result, we have now two distinct ways of thinking about causal analysis and the corresponding methodological advice in the Social Sciences. Mahoney and Goertz (2006, forthcoming) argue that these methodological developments reveal that there are two distinct cultures in the Social Sciences - a quantitative and a qualitative culture - with separate approaches to explanation, different conceptions of causation and corresponding different methodologies and research practices, e.g. in respect to case selection and generalization. Nevertheless, many scholars who perceive their work as "qualitative research" will certainly reject Mahoney and Goertz' implication that an approach to causal analysis that is based on settheory and "configurational thinking" (Ragin 2008: 109-123) represents qualitative research. In the German speaking countries, and probably in continental Europe in general, "qualitative research" is much more associated with hermeneutics and interpretative methods based on a constructivist or conventionalist epistemology. Furthermore, recent text books on qualitative analysis and case study research (Blatter, Janning and Wagemann 2007, Blatter and Haverland forthcoming) emphasize the plurality of epistemological foundations, methodological approaches and specific techniques that are useful and legitimate in the Social Sciences. But emphasizing the plurality of legitimate research approaches is only the first step towards a productive combination of research methods. A further, necessary second step is to strive for an "epistemological middle ground" that rejects any fundamentalist epistemological positions and the corresponding claims of incommensurability (Blatter and Haverland forthcoming). The articles in this issue of GPS represent the diversity of methodological developments within "qualitative research" and show how it is possible to move towards the "middle ground" between positivist, constructivist and realist epistemologies. The first two contributions show how far we got in respect to applying the new methodological tools in causal analysis. They address the two major methodological innovations in causal analysis - configurational thinking and causal process tracing - but they do this quite differently. Rihoux, Rezsohazy and Bol (2011) present an extensive review of the existing studies which apply Qualitative Comparative Analysis (QCA) as technique for revealing causal configurations. Julian Junk (2011) presents a paper on the combinations of methods in the form of both triangulation and parallelization. From a methodological point of view the most interesting (and somehow puzzling) result is the fact that most scholars who apply QCA do this in a way that Rihoux, Rezshohazy and Bol call "inductive" or "soft theoretical. " Within their theoretical framework, many scholars actually do not deduce causal configurations from theories but delineate a set of potential causal conditions like a set of "independent" (in both senses: explanatory and autonomous) variables in statistical analysis. …