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In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

An 8-Hour Livestream Seminar Taught by Tasha Fairfield, Ph.D.

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.

By the end of the course, you will be able to read qualitative case studies more critically and apply Bayesian reasoning in your own research to make better inferences.

In this short-course, you will learn:

• Guidance for crafting clearly articulated mutually exclusive hypotheses.
• Skills for avoiding common cognitive biases when analyzing evidence.
• Skills for thinking about the most informative kinds of evidence to seek that will help you figure out which hypothesis provides the best explanation.
• Procedures for evaluating how strongly an evidentiary observation favors one hypothesis over a rival.
• An appreciation for the wide range of nuanced and inherently qualitative evidentiary observations that can be valuable for inference to best explanation.
• Prescriptions for assessing how much uncertainty surrounds your conclusions.
• An understanding of how Bayesian reasoning builds on common sense and differs from other commonly-used methodologies, along with the advantages it affords for rational inference, knowledge accumulation, and communicating findings to laypeople.