Prof. Andreas Schwab (Iowa State University)
- Date: Wednesday, October 16th, 2024
- Time: 2:00 pm - 3:30 pm
- Location: Kaulbachstr. 45, Room 202
- Title: Bayesian Analysis: Conceptual Introduction
- Abstract: This workshop 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 workshop 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 workshop 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 workshop 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]