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Computational Social Science Methods (SPS-WS-MU-COM-24)

SPS-WS-MU-COM-24

Description

This interactive course introduces the principles of Bayesian reasoning, with applications to process-tracing, comparative case studies, and multimethod research.  Participants will learn how to construct well-articulated rival hypotheses to compare, systematically assess the inferential weight of qualitative evidence, avoid common cognitive biases that can lead to sloppy reasoning, and evaluate which hypothesis provides the best explanation through Bayesian updating.  The course will also address key aspects of research design.  We will further explore the potential for Bayesianism to serve as a bridge between quantitative and qualitative research.  Throughout, we will conduct a wide range of exercises and group work to give participants hands-on practice applying Bayesian techniques.  Upon completing the course, participants will be able to read case studies more critically, evaluate whether and to what extent the evidence presented supports the authors’ conclusions, and apply Bayesian principles in their own research.
 

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