Chapter 2 presents the theoretical framework and the research design of the study. It examines how political parties’ programmatic commitment shapes the expansion and quality of healthcare reforms in post-neoliberal Latin America. While democracy, ideology, and electoral competition explain why reforms are initiated, the chapter argues that the key to understanding reform outcomes lies in whether parties possess programmatic commitment – that is, if their core ideological values are directly tied to healthcare. Where party values align with healthcare expansion, parties actively shape, support, and implement reforms (a programmatic path), leading to higher-quality, sustainable outcomes with clear funding and infrastructure plans. In contrast, when parties lack such commitment, technocrats without partisan ties drive the policymaking process (a technocratic path), resulting in reforms that may expand formal coverage but often lack robust political support, funding stability, and effective implementation. The chapter also details the research design, describing the operationalization of the dependent and independent variables, the process-tracing approach used to collect evidence, and the logic of the case selection. Data collection combined extensive interviews and archival research. Ultimately, the chapter highlights the need to analyze policy engagement at each stage of the process.

This chapter traces commitment and capacity’s ability to facilitate local parish intervention in the process of Drug War violence. The chapter begins by introducing an original, parish-based survey of Drug War response, with an index comprised of thirteen discrete actions parishes could take to protect communities. Quantitative measures of commitment and capacity from the prior chapters independently correlate with more comprehensive Drug War response in parishes. Next, to gain a more textured idea of how commitment and capacity interact to speed or impede Drug War response, the authors present four extended parish case studies to capture representative combinations of commitment and capacity (low-low, low-high, high-low, and high-high). The chapter’s final section probes two “outlier” cases where protective interventions were higher than the initial combination of commitment and capacity would predict, emphasizing the role of contingency and agency in Drug War response.

Process tracing methods, which involve ways of using information within a single case to think carefully about questions of social-science theory, have become one of the leading qualitative methods in political science and related fields. Can mixed-method approach to process tracing work without destroying the qualitative logic of the method? This chapter argues that two distinct forms of process tracing, involving testing of existing causal theories and discovery of new theories, each have room for mixed-method strategies. We show that qualitative components can become steps within a testing-based process tracing argument, and offer guidelines for structuring such studies. We then offer a novel approach to combining machine-learning techniques like LASSO regression with discovery-based process tracing, illustrating the design with an application to the rise in visibility of U.S. far right broadcaster Alex Jones.

This chapter introduces qualitative causal inference and explanation, a hybrid method that integrates qualitative case-study analysis with elements of quantitative causal inference. Traditional process tracing emphasizes explanation but risks false positives, while identification-based inference secures validity but offers little guidance for explaining particular outcomes. The chapter argues that adapting core ideas from causal inference – such as identification and bias – into a qualitative idiom can strengthen within-case analysis. It defines qualitative causal inference as a strategy for distinguishing true causal influences from spurious associations and sets out criteria for valid unit-level inference. By combining explanation with valid inference, this approach aims to produce more credible and more satisfying accounts of complex outcomes such as democratic transitions, revolutions, and wars.

This chapter compares process tracing with qualitative causal inference to clarify their methodological domains, overlaps, and divergences. Process tracing, rooted in the detective model of inference, is shown to be a valid reasoning strategy within its epistemic domain – situations where background knowledge permits the substitution of a bias-free causal graph for a bias-saturated one. However, outside this narrow domain, particularly in the social sciences, process tracing is prone to false positives because it assumes away potential bias. The chapter surveys four variants of process tracing: orthodox, Bayesian, sequential, and mechanistic. While each represents an important development, all share a vulnerability to unacknowledged systematic error when applied to causal inference. Examples from published research illustrate how even methodologically rigorous applications of process tracing may inadvertently generate false positives. By contrast, qualitative causal inference was designed to address this problem by explicitly testing for causal continuity along the front-door path and causal discontinuity along bias-inducing paths. The chapter concludes by weighing the relative strengths of the two approaches. While process tracing remains useful in domains where causal structures are well understood, qualitative causal inference offers a more robust framework for within-case causal analysis in the complex and uncertain settings typical of social science research.

This chapter describes the study’s research design. Protest event data from the three contexts over three decades, gathered with an intensive data collection strategy, was aggregated into a dataset of forty large far-right demonstration campaigns. The chapter describes the application of qualitative comparative analysis (QCA) and process-tracing techniques to detect patterns of causal conditions and then to evaluate rigorously the processual causation underlying those patterns. In social movement research and scholarship on the far right, single-case studies abound, providing rich qualitative depth but limited generalisability. At the same time, large-N quantitative research often lacks that depth of information that enables robust causal inferences. The main thrust of the research design applied here is overcoming these deficiencies.

This chapter traces the demobilisation process of the British National Party’s Red, White, and Blue festival campaign. The party had mobilised this campaign in attempts to reform its image and boost its electoral prospects, wanting to be known as family-friendly rather than fascist. The qualitative comparative analysis (QCA) locates it in the anti-far-right militancy pattern of demobilisation. By tracing the case, the chapter confirms the causation implied by the QCA pattern: militant counter-mobilisation drove a diminishing returns mechanism whereby increased costs of maintaining the campaign combined with decreased benefits, both directly resulting from private coercion, caused demobilisation.

This chapter traces the demobilisation process of the German People’s Union (Deutsche Volksunion, DVU) Congress campaign. The party mobilised this campaign at the Hall of the Nibelungs (Nibelungenhalle) in Passau as a means of supporting its electoral prospects as well as propagating revisionist historical narratives championed by the party leader. This case represents the largest grouping of negative demobilisation uncovered by the qualitative comparative analysis (QCA), ‘civil counter-mobilisation’. Tracing the case helps identify a minimally sufficient causal explanation. The campaign faced private channelling that combined with internal factors to reduce participation in the campaign, as well as the removal of its preferred demonstration venue when local authorities approved the demolition of the Hall of the Nibelungs.

This chapter traces the demobilisation process of the Ulrichsberg Celebration campaign in Austria. Veterans of Third Reich military forces had mobilised the campaign with the intention of commemorating Wehrmacht as well as Waffen-SS soldiery, asserting that comradeship is sacred, regardless of whether it was in the service of Nazi aggression and criminality. The qualitative comparative analysis (QCA) finds it covered by the closing opportunity pattern of demobilisation. Process tracing shows that the case was causally overdetermined with two potentially sufficient demobilisation causes present. However, there are good circumstantial grounds to conclude that the causation implied by the QCA pattern was decisive, in that a mechanism of resource deprivation was at work once the state withdrew essential material support for the campaign, causing demobilisation through closing opportunity.

We assume that causes contribute to their effects through continuous processes, generally involving intermediate steps. Such processes may be represented by boxes-and-arrows diagrams which express simple ‘theories of change’. We illustrate this using examples from Barbados’s sugar-sweetened beverages tax. The formal relations outlined in Chapter 1 dictate that causation cannot be cyclical. So graphs with cycles needn’t be inconsistent – they just need to be unwound with careful temporal labels to distinguish each time ‘the same’ feature occurs. Developing theories of change often involves starting with relatively simple ones, then testing and refining their contents..

We suppose that, stage by stage in causal processes, causes engage in activities that bring about their effects. These should be maximally vividly described when representing causal processes. We thus follow G. E. M. Anscombe, recent work in philosophy of biology on mechanisms, process tracing and realist evaluation in rejecting the Humean approach to causation, which eschews activities. Activities are interpreted as continuous processes afforded by the arrangements of the underlying system in which they take place. We illustrate here how to include activities in our original ‘boxes-and-arrows’ diagrams.

This chapter introduces decision-making criteria for the three core choices of qualitative research design: the number of cases to study, which cases to study, and which particular analysis technique to use. Background research is important in identifying potential cases to study, especially cases that represent negative evidence, and in making case selections. Negative evidence plays a key role in maximizing the credibility of hypothesis tests by creating variation in the values of key variables. Common techniques in qualitative analysis include process tracing, content analysis, analytic narratives, structured focused comparison, and methods of similarity; principles of research design and case selection vary across these techniques.

Both qualitative and quantitative methods provide rigorous ways to establish and assess causality, though their understandings of causality and techniques for doing so differ. Qualitative and quantitative techniques are generally complementary rather than substitutes or opposites. Common quantitative tools include cross-tabulation, regression, and logit/probit models; which is appropriate typically depends on the level of measurement of the dependent variable. Common qualitative tools include two within-case techniques, process tracing and analytic narratives, and three between-case techniques, case control, structured focused comparison, and content analysis. “Intermediate-N” techniques exist for research questions whose number of cases is greater than that typically used for qualitative analysis but below the threshold for successful quantitative analysis, along with Big Data approaches for extreme-N analysis, content analysis techniques for corpora, and mixed methods approaches.