Bayesian Inference for Process Tracing and Comparative Analysis

Speaker

Tasha Fairfield, London School of Economics and Political Science

Abstract

Bayesian probability provides a rigorous foundation for qualitative research that mirrors the way scholars naturally approach inference, while helping avoid cognitive biases that can lead to sloppy reasoning. This talk introduces the basic principles of Bayesian reasoning as elaborated in Social Inquiry and Bayesian Inference (CUP 2022). It then offers a unified Bayesian perspective on within-case (process tracing) and cross-case (comparative) analysis for qualitative research, with an application based on published case-study research. Methodological literature often treats these as distinct analytical endeavors that draw on different logics of inference. Within a Bayesian framework, however, there are no fundamental distinctions; all evidence contributes to inference in the same manner, whether we are studying a single case or multiple cases. In essence, each piece of evidence we find weighs in favor of one explanation over a rival to some degree, which we assess by asking which explanation makes that evidence more expected. Evidentiary weight then aggregates both within any given case, and across different cases that fall within the scope of the theories we are testing. This Bayesian approach simplifies our understanding of inference, and more naturally mirrors the way that scholars learn from cumulative knowledge.

Chair

Katia Pilati, University of Trento

Presenter 

Giuseppe Veltri, University of Trento