This chapter lays out the study’s research design. The design aims to enhance cycles of silence theory’s generalizability at two levels. At a macro level, the goal is to increase the potential that, contingent on local factors, the theory applies to as many of the communities facing criminal group violence as possible. It does so by drawing on logic derived from human social psychological dynamics, leveraging a wide range of existing datasets including a global survey of 109,000 citizens, and studying communities both the Global North (Baltimore, Maryland) and Global South (Lagos, Nigeria). At a micro level, the design combines cross-national data with original surveys as well as interviews and first- hand observations in Baltimore and Lagos. This multimethod approach improves the likelihood that the findings from the surveys and interviews in Baltimore and Lagos accurately reflect cooperation dynamics in the cities. Finally, the chapter provides definitions for key terms related to the study’s main actors – criminal groups, police, and citizens – and the main outcome of citizen cooperation with the police.

We connect the literature on causal models to qualitative inference strategies used in process tracing. The chapter outlines a procedure for drawing case-level causal inferences from a causal model and within-case evidence. We also show how a key result from the causal-models literature provides a condition for when the observation of a node in a causal model (a “clue”) may be (or certainly will not be) informative, and we extract a set of implications for process-tracing methods.

We apply the causal-model-based approach to process tracing to two major substantive issues in comparative politics: the relationship between inequality and democratization and the relationship between institutions and growth. Drawing on case-level data, we use qualitative restrictions on causal types together with flat priors to draw inferences about a range of causal queries. The applications illustrate the different types of learning that can be gleaned from information on moderators and mediators, as well as the scope for learning from historical data when researchers have informative beliefs about confounding processes.

This chapter argues for the utility of causal models as a framework for choosing research strategies and drawing causal inferences. It provides a roadmap for the rest of the book. The chapter highlights the approach’s payoffs for qualitative analysis, for combining intensive and extensive empirical strategies, and for making research design choices.

Silence permeates intimate spaces and intimate relationships. Such intimate silences shape social action, which can establish and maintain fundamental inequalities. The unsaid can be difficult to identify, especially in such contexts. However, by showing how we “tiptoe” around particular topics, a convincing case for the unsaid can be made. Using two case studies, we show how triangulation can be used to make the unsaid noticeable. The first case study is situated in post-apartheid South African paid domestic labor, where dyadic research was used to reveal silences around the topics of black sexuality, male visitors, and employer monitoring. These silences work to maintain privileged white control over intimate black activities. The second case study occurs in the undocumented student movement in the United States, where a comparison between public utterances and individual interviews revealed an absence of talk around intimate partner violence within the public discourses of undocumented students. This absence works to keep inequalities around ongoing domestic violence among undocumented immigrants invisible and unaddressed. In both cases, it was only through multimethod research that the absences and dissonances across the data sets were noticed, allowing for new understandings of the issues and their fundamental inequalities.