Hi everyone,

Thanks again for a thoughtful and focused session today. We picked up where we left off and moved into the core of QCA’s analytic engine: the construction and interpretation of truth tables. This step translates calibrated data into configurations by grouping cases based on their shared combinations of conditions. Each row in the truth table represents one unique combination, along with its associated outcome.

We discussed the use of consistency and frequency thresholds as filters to determine which configurations are robust enough to carry forward. Configurations that meet these thresholds are considered sufficient; those that do not are either omitted or flagged for further reflection.

This led us into the concept of logical remainders i.e., configurations that are logically possible but not observed in the data. These play an important role in Boolean minimization, where the goal is to reduce complex expressions of configurations into simpler ones without losing explanatory value.

We also discussed three main solution types in QCA:

• The complex solution uses only empirically observed configurations and makes no assumptions beyond the data.

• The parsimonious solution uses all logical remainders to simplify configurations, even if they lack theoretical support.

• The intermediate solution strikes a balance by including only those remainders that are theoretically plausible, guided by researcher's heuristics.

These solution types do not compete with one another but serve different analytic purposes depending on the goals and context of the study. They also help distinguish core from contributing conditions in a configuration.

We also spent time analyzing truth tables from Greckhamer and Gür’s (upcoming) study on the airline industry. This helped ground our discussion in a real empirical application. We explored how certain configurations might warrant iterative refinement, especially when empirical inconsistencies or theoretical tensions arise. As part of that, we discussed the role of counterfactuals in QCA, particularly how they enter the picture through logical remainders. We considered when it is appropriate to invoke them, how plausibility is assessed, and how theoretical justification becomes crucial when choosing which assumptions to include in an intermediate solution.

Lastly, we briefly touched on the use of heuristics in QCA, drawing from the Misangyi et al. (2017) paper, where they help guide the treatment of counterfactuals and support more grounded interpretation during solution minimization.

Looking forward to hearing what others are taking away from this part of the method.