In the last decades, ‘research design’ has become a strategic topic across political science. An emerging discourse relies on it to encompass paradigmatic oppositions and cultivate a pluralist approach to causation. As an introduction to the special issue on the topic, we offer an outline of the roles that the discipline recognizes to design in its relation to models and contend that, in a time of fascination for predictors, political science pluralism allows for balancing interpretability and validity of findings at once.

Qualitative scholars exhibit a wide range of views on and approaches to causality. While some approaches reject causality from the outset, a large strand of qualitative research in political science and international relations does, however, pursue causal explanation. Qualitative scholars nevertheless disagree about what causality means. Our paper reviews what causality means within different strands of qualitative research and how qualitative scholars engage in causal explanations. We focus particular attention on the fertile middle ground between qualitative research that seeks to mimic the statistical model and research that rejects causality entirely. In broad strokes, we understand views of causality as lying on a spectrum and partly overlapping. Along the spectrum, we identify three main clusters: ‘positivist leaning,’ ‘postpositivist leaning,’ and ‘interpretivist leaning.’ Within each cluster, we identify the main traits and provide illustrative examples. We find merit in each of these three clusters of approaches and in the ongoing dialogue among qualitative scholars of different orientations. Understanding similarities and differences in the way various scholars address causality might encourage some to take steps along the spectrum and expand their repertoires to embrace elements of other approaches. By making these distinctions more explicit, we hope to be able to enhance our understanding of different views of causality and the extent to which they overlap and provide the potential for collaboration.

How can Qualitative Comparative Analysis contribute to causal knowledge? The article's answer builds on the shift from design to models that the Structural Causal Model framework has compelled in the probabilistic analysis of causation. From this viewpoint, models refine the claim that a ‘treatment’ has causal relevance as they specify the ‘covariates’ that make some units responsive. The article shows how QCA can establish configurational models of plausible ‘covariates’. It explicates the rationale, operations, and criteria that confer explanatory import to configurational models, then illustrates how the basic structures of the SCM can widen the interpretability of configurational solutions and deepen the dialogue among techniques.

How does space matter in our analyses? How can we evaluate diffusion of phenomena or interdependence among units? How biased can our analysis be if we do not consider spatial relationships? All the above questions are critical theoretical and empirical issues for political scientists belonging to several subfields from Electoral Studies to Comparative Politics, and also for International Relations. In this special issue on methods, our paper introduces political scientists to conceptualizing interdependence between units and how to empirically model these interdependencies using spatial regression. First, the paper presents the building blocks of any feature of spatial data (points, polygons, and raster) and the task of georeferencing. Second, the paper discusses what a spatial matrix (W) is, its varieties and the assumptions we make when choosing one. Third, the paper introduces how to investigate spatial clustering through visualizations (e.g. maps) as well as statistical tests (e.g. Moran's index). Fourth and finally, the paper explains how to model spatial relationships that are of substantive interest to some of our research questions. We conclude by inviting researchers to carefully consider space in their analysis and to reflect on the need, or the lack thereof, to use spatial models.

A primary challenge for researchers that make use of observational data is selection bias (i.e. the units of analysis exhibit systematic differences and dis-homogeneities due to non-random selection into treatment). This article encourages researchers in acknowledging this problem and discusses how and – more importantly – under which assumptions they may resort to statistical matching techniques to reduce the imbalance in the empirical distribution of pre-treatment observable variables between the treatment and control groups. With the aim of providing a practical guidance, the article engages with the evaluation of the effectiveness of peacekeeping missions in the case of the Bosnian civil war, a research topic in which selection bias is a structural feature of the observational data researchers have to use, and shows how to apply the Coarsened Exact Matching (CEM), the most widely used matching algorithm in the fields of Political Science and International Relations.

The article offers an overview of the use of survey experiments in political research by relying on available examples, bibliographic data and a content analysis of experimental manuscripts published in leading academic journals over the last two decades. After a short primer to the experimental approach, we discuss the development, applications and potential problems to internal and external validity in survey experimentation. The article also provides original examples, contrasting a traditional factorial and a more innovative conjoint design, to show how survey experiments can be used to test theory on relevant political topics. The main challenges and possibilities encountered in envisaging, planning and implementing survey experiments are examined. The article outlines the merits, limits and implications of the use of the experimental method in political research.

Regression discontinuity (RD) designs have become increasingly popular in political science, due to their ability to showcase causal effects under weak assumptions. This paper provides an intuition-based guide for the use of the RD in applied research. After an intuitive explanation of how the method works, we provide a checklist that can help researchers understand the main robustness checks they should run, and a quick introduction to software implementing the design. We also provide a list of classic designs and examples of their application in political science. We hope this article can constitute a stepping stone from which researchers interested in RD can jump to more advanced literature; and which makes researchers not interested in implementing RDs better consumers of research employing this design.