Bayesian Analysis

Bayesian Analysis

Dr. Andreas Schwab

Iowa State University

January 26, 2024

12:00 PM EST (New York), 4:00 PM GMT (London)

Presentor Biography

Dr. Andreas Schwab, an Associate Professor at Iowa State University’s Ivy College of Business, focuses on multilevel learning in entrepreneurial ecosystems, particularly digital platforms, project ventures, and women entrepreneurs. He spearheads methodological advancements in management by advocating alternatives to statistical tests, introducing Bayesian statistics, and supporting replications. His research has appeared in esteemed journals like Academy of Management Journal, and he serves on editorial boards, including Entrepreneurship Theory & Practice. Andreas is an Ambassador of the Center of Open Science, and his research is funded by notable institutions like the U.S. Fulbright Program and Australian Research Council. He holds a Ph.D. in management from the University of Wisconsin – Madison.


This webcast will offer an insightful introduction to Bayesian analysis, a method of empirical data examination that applies Bayes’ theorem to update existing knowledge about model parameters based on newly collected data. The webcast will cover the following topics:

  1. The Basics: Understand the fundamental conceptual nature of Bayesian approaches and their potential advantages over statistical significance tests.
  2. Parameter Estimation: Conceptually outline the steps of Bayesian parameter estimation, applying Markov-Chain Monte Carlo simulations.
  3. Prior Distributions: Discuss the function and value of prior distributions in Bayesian analyses.
  4. Posterior Distributions: Learn how to interpret Bayesian posterior distributions for hypothesis testing, prediction, and theory building.
  5. Communication and Reporting: Learn about the standards for communicating and reporting Bayesian analyses and results for publication in top management journals.

While the webcast will mention various software packages available for Bayesian analyses, it will not delve into the intricacies of related analytic choices and their coding during Bayesian estimation processes. Instead, the focus is on equipping participants with a basic conceptual understanding of Bayesian analysis, its benefits for hypothesis testing and theory building, and providing actionable advice on conducting and publishing high-quality Bayesian management studies. This webcast is a must-attend for those seeking to enhance their understanding and application of Bayesian analysis in management studies.

Additional Resources:

McCann, B. & Schwab. A. (2023).  Bayesian Analysis in Strategic Management Research: Time to Update Your Priors. Strategic Management Review, Vol. 4(1): 75–106; DOI: 10.1561/111.00000053

Kruschke, J. K., Aguinis, H., & Joo, H. (2012). The Time Has Come: Bayesian Methods for Data Analysis in the Organizational Sciences. Organizational Research Methods, 15(4), 722-752.

McElreath, R. (2020). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press. [Textbook Webpage, Lecture series]

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