Bayesian Analysis for Qualitative Evidence: A Short Course

Qualitative evidence can make vital contributions to social science research that strives for explanation. Diverse kinds of qualitative information, including but hardly limited to interviews, ethnographic observations, news reports, meeting notes, and archival records, provide “clues” that help us adjudicate between alternative explanations, in the same way that a detective goes about figuring out who among a list of plausible suspects committed the crime, how, and why. Yet qualitative studies do not always draw clearly reasoned and well justified conclusions from the evidence presented. Authors often overstate their claims, and as we know from cognitive psychology, multiple biases can lead to faulty reasoning.

Bayesianism is enjoying a revival across many fields, and it offers a powerful tool for improving inference and analytic transparency in qualitative research. Bayesian inference is an intuitive process that begins by assessing prior odds on rival hypotheses, drawing on any relevant initial knowledge we have. We gather evidence and evaluate its inferential weight by asking which hypothesis makes the evidence more expected. We then update to obtain posterior odds on our hypotheses—following Bayes’ rule, we gain more confidence in whichever hypothesis makes the evidence more expected.

This course will provide concrete guidance on how to carry out each step of the Bayesian reasoning process, with concrete applications to case studies and multi-methods research drawn from a wide range of fields. You will learn how to construct rival hypotheses, assess the inferential weight of evidentiary observations, and evaluate which hypothesis provides the best explanation through Bayesian updating. This course aims to include ample discussion, along with exercises and breakout-group opportunities to give you hands-on practice with applying Bayesian techniques.

Starting September 5, we are offering this seminar as an 8-hour synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two 2-hour lecture sessions which include hands-on exercises, separated by a 30-minute break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.

*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously. 

Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.

If you are looking to learn more about Bayesian methods and applications, check out the next seminar in our Distinguished Speaker Series, “Science Before Statistics: Introduction to Bayesian Causal Inference”, taught by Richard McElreath on September 25.