Much CA research is grounded in specimen collections, which are numerically modest by the standards of survey research or corpus linguistics, but substantial relative to observational fieldwork. The appeal of collection-based methods is that they afford some of the advantages of context-sensitive case analysis, while also enabling the development of accounts whose generality may be tested across a number of cases. They have a particular utility for the investigation of novel phenomena in areas whose elementary units and basic organizational forms are not well-understood. This chapter reflects on key issues involved in both assembling and working through specimen collections. Regarding the assembly of cases, it is argued that researchers should cast a wide net across a diversity of data sources, taking care to avoid allowing hunches or hypotheses to gain a controlling influence over data collection. Regarding the investigation of patterns across cases, the discussion touches on the utility of single case analyses, systematic reviews of the entire collection, and various approaches to dealing with anomalous cases. The chapter concludes with a discussion of the limitations of prototypical specimen collections, identifying conditions when it may be advisable to augment a collection by adding cases beyond the target phenomenon.

Political scientist Cammett considers the use of positive deviant cases – examples of sustained high performance in a context in which good results are uncommon – to identify and disentangle causal complexity and understand the role of context. Although the consensus view on the role of deviant cases is that they are most useful for exploratory purposes or discovery and theory building, Cammett suggests they can also generate insights into the identification and operation of causal mechanisms. She writes that “analyses of positive deviant cases among a field of otherwise similar cases that operate in the same context … can be a valuable way to identify potential explanatory variables for exceptional performance.” The hypothesized explanatory variables can then be incorporated in subsequent quantitative or qualitative studies in order to evaluate their effects across a broader range of observations. The chapter discusses how to approach selection of positive deviant cases systematically and then works through a real example.

It significantly strengthens the inferences drawn based on QCA results if we connect these results to theoretical knowledge and within-case evidence before, during, and after the analysis. In this chapter, we discuss two prominent tools of doing so after the analytic moment – set-theoretic theory evaluation and set-theoretic multi-method research (SMMR) – and demonstrate their implementation within R. Theory evaluation is a form of re-assessing theoretical hunches based on the results generated by QCA. While it can also be used for the identification of interesting cases for follow-up case studies, this task is better achieved with set-theoretic multi-method research. The latter is a tool for identifying typical and deviant cases for comparative or single within-case analysis.

Learning goals:

- Basic understanding of what theory-evaluation and set-theoretic multimethod research are.

- Familiarity with how to apply formal set-theoretic theory evaluation for re-assessing theoretical hunches based on the results generated by QCA.

- Familiarity with how to use set-theoretic multi-method research (SMMR) for the identification of cases for follow-up case studies after QCA.

- Ability to implement theory evaluation and SMMR in R.