Process Tracing (QMMR 1)
Andrew Bennett, Georgetown University; Jeffrey Checkel, European University Institute; Tasha Fairfield, London School of Economics
This short course will cover the underlying logic and best practices of process tracing, which is a within-case method of developing and testing causal explanations of individual cases.
We will briefly summarize the philosophy of science behind explanation via reference to hypothesized causal mechanisms and then outline the logic of process tracing, which entails asking whether the evidence we find in a case would be more or less plausible if a given explanation of that case is true as compared to a rival explanation. Throughout the session we will emphasize best practices and applications to exemplars of process tracing research. The examples we use will be primarily in international relations and comparative politics, but the methods we discuss are applicable to all the subfields of political science, to sociology, economics, history, business studies, public policy, and many other fields. Students will practice applying process tracing reasoning in small group exercises. As time allows, and depending on the numbers, students will discuss how they plan to use process tracing in their current research so the instructors and fellow students can offer constructive advice on how best to carry it out.
The course will also introduce the logic of Bayesian inference that underlies process tracing and overview key conceptual insights that can help us better evaluate the inferential import of qualitative evidence. Students interested in learning more about the Bayesian approach are encouraged to also take the ‘Bayesian Process Tracing’ short course led by Tasha Fairfield, which will be held in the afternoon of the same day as the present course. Students can benefit by taking either or both courses; we have designed the two short courses so that they complement each other.
Bayesian Process Tracing (QMMR 2)
Tasha Fairfield, London School of Economics
This short course outlines the logic of Bayesian process tracing and provides students with practical advice, examples, and exercises to enable them to use this method in their work. It builds on Social Inquiry and Bayesian Inference: Rethinking Qualitative Research, by Tasha Fairfield and Andrew Charman (Cambridge University Press, 2022).
The course does not require any prior training in process training, Bayesianism, probability theory, or logic. The only math skills that will be assumed are basic arithmetic. The course is designed to complement the APSA short course led by Andrew Bennett, Jeffrey T. Checkel, and Tasha Fairfield, but each course can also be usefully taken independently from the other.
The core idea that motivates the course is that the way we intuitively approach qualitative case research is similar to how we read detective novels. We consider various different hypotheses to explain what occurred—whether the emergence of democracy in South Africa, or the death of Samuel Ratchett on the Orient Express—drawing on the literature we have read (e.g. theories of regime change, or other Agatha Christie mysteries) and any salient previous experiences we have had. As we gather evidence and discover new clues, we continually update our beliefs about which hypothesis provides the best explanation—or we may introduce a new alternative that occurs to us along the way.
Bayesianism provides a natural framework that is both logically rigorous and grounded in common sense, that governs how we should revise our degree of belief in the truth of a hypothesis—e.g., “mobilisation from below drove democratization in South Africa by altering economic elites’ regime preferences,” (Wood 2001), or “a lone gangster sneaked onboard the train and killed Ratchett as revenge for being swindled”—given our relevant prior knowledge and new information that we obtain during our investigation. Bayesianism is enjoying a revival across many fields, and it offers a powerful tool for improving inference and analytic transparency in qualitative research.
This course introduces basic principles of Bayesian reasoning with the goal of helping us leverage common-sense understandings of inference and improve intuition when conducting causal analysis with qualitative evidence. We begin by introducing the general logic of Bayesian inference, that is, how we can update our prior view about which explanation is more plausible when we learn new evidence about our cases. We explain the importance of developing mutually exclusive explanations and discuss how to formulate well-constructed hypotheses to compare. We then elaborate practical procedures for evaluating the inferential import of the evidence by assessing its likelihood under rival hypotheses and weighing the totality of evidence to update our prior views about which hypothesis provides the best explanation. We include multiple examples and exercises drawing on published case studies from comparative politics and international relations to show how this updating process works in practice with real-world qualitative evidence.
Studying Causal Mechanisms Using In-Depth Case Studies (QMMR 3)
Derek Beach, Aarhus University
The study of causal mechanisms is ubiquitous in the social sciences. Mechanism-focused research using in-depth case studies enables us to gain a better understanding of how things work and under what conditions using real-world cases instead of gaining knowledge about mean causal effects across cases based on experimentally manipulating treatments in controlled populations. However, the potential gains of mechanism-focused research have not been fully reaped in the social sciences because of the tendency to reduce mechanisms to counterfactuals which are then investigated using cross-case comparisons.
Inspired by recent developments in mechanism-focused research in medicine and policy evaluation (Clarke et al, 2014; Cartwright and Hardie, 2012), the first session of the course will discuss the standards developed in the natural sciences for what constitutes a ‘good’ mechanistic explanation (e.g. Craver and Darden, 2013), and how these can be translated into social science theorization. The second session will then present the developing standards in the natural sciences for what constitutes ‘good’ mechanistic evidence, and again how these can be translated into the social sciences. The final session discusses practical applications, including how mechanism-focused research can be used as an adjunct method to improve social science experiments in designing the experiment and interpreting the data.
Managing and Sharing Qualitative Data (QMMR 4)
Sebastian Karcher, Syracuse University
Research data management entails developing a data management plan and handling research materials systematically throughout the research lifecycle. Effectively managing data makes research more robust, allows data to be useful over a longer period of time, and facilitates sharing data with the broader research community. This short course equips participants with a range of strategies for effectively managing qualitative data. Hands-on exercises allow participants to practice basic data management tasks in the context of their own projects. The short course particularly emphasizes writing data management plans (DMPs), as required by the National Science Foundation (NSF) and other funders and organizations, for research involving qualitative data. We also consider the benefits and challenges of sharing data and demonstrate appropriate techniques for mitigating them, again with the help of exercises and tools that participants will be able to use with their own research. Finally, the short course introduces and briefly discusses new techniques for making qualitative research more transparent, including developing interview methods appendices and tables, documenting analysis performed in qualitative data analysis (CAQDAS) software, and employing Annotation for Transparent Inquiry (ATI).