We illustrate Bayesian mixed methods with causal models through a reexamination of the model of inequality and democratization and of institutions and growth introduced in Chapter 8. We show how to use updated population models to draw both population- and case-level inferences, demonstrate situations in which learning is minimal and in which it is more substantial, and illustrate how the probative value of case-level evidence can be empirically established through model updating.

This chapter extends the analysis from Chapter 7 to multi-case settings and demonstrate how we can use the approach to undertake mixed-method analysis. We show how, when analyzing multiple cases, we can update our theory from the evidence and then use our updated theory to draw both population- and case-level inferences. While single-case process tracing is entirely theory-informed, mixed-data inference is thus also “data”-informed. We show how the approach can integrate information across any arbitrary mix of data structures, such as “thin” data on causes and outcomes in many cases and “thicker” process evidence on a subset of those cases.

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.