PCW #6 - Applied Bayesian Statistics (64882)

June 3, 2021 10:00 - 13:00 Part I- June 3, 2021 14:00 - 17:00 Part II
Virtual Event

PCW #6 - Applied Bayesian Statistics (64882)

Presented by:

 Dr. Milica Miocevic

Sponsored by:


Continuing Education Credits:

6 CE Credits 






6 Hours - (Two three-hour sessions)

Target Audience:

Researchers, faculty members, and graduate students in the social sciences

Skill/Difficulty Level:


Workshop Description:

Bayesian methods are being suggested as a remedy for many issues in psychological research, ranging from insufficient transparency to lack of convergence of complex models with small samples. This workshop will introduce philosophical underpinnings of Bayesian statistics and cover steps in fitting regression and mediation models in the Bayesian framework. The workshop will consist of lectures and practical sessions in which attendees will practice steps in conducting Bayesian analyses in R. Participants are encouraged to bring their own data for the lab portion of the workshop, however, the instructor will provide example data sets for participants who do not have their own data. Upon the completion of the workshop, participants will be able to use Bayesian linear regression and mediation analysis in their own research, encode existing prior information for model parameters, diagnose convergence in Markov Chain Monte Carlo estimation, write up the results of Bayesian analyses for a journal article, and understand articles that examine and apply Bayesian methods.

Please note that this workshop is presented in 2 parts. June 3, 2021, 10:00 - 13:00 Part I and  June 3, 2021, 14:00 - 17:00 Part II. learners must register for and attend both parts of the course to receive credit

Learning Outcomes:

  1. Researchers will be able to run Bayesian models in R on their own data
  2. Researchers will learn the appropriate steps in a Bayesian analysis
  3. Researchers will learn how to correctly implement Markov Chain Monte Carlo estimation
  4. Researchers will learn how to report findings from a Bayesian analysis for a journal article
  5. Researchers will be able to understand and review papers that used Bayesian statistics