Statistical Methods Course Description
Overview: Although the ideas of inverse probability and Bayes’ theorem have been longstanding within mathematics, these tools have not been at the forefront of modern-day applied statistics. Specifically, in the 20st century the statistical tools most often used by researchers in the field of psychology are based on frequentist statistics (i.e., p-values and null hypothesis testing). Since the beginning of the 21st century, however, Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research.
This increase is specifically due to recent computational advancements and the availability of Bayesian estimation methods in popular software and programming languages like Mplus, and Blavaan. Moreover, the use of Bayesian methods has increased because this estimation framework can handle some commonly encountered problems in orthodox statistics. For example, Bayesian methods can be used for, e.g., producing more accurate parameter estimates and aiding in situations where only small sample sizes are available. Alternatively, some researchers implement Bayesian methods simply because they like the methodology, or believe in the Bayesian way of updating knowledge with new data instead of testing the null hypothesis over and over again assuming nothing is going on in the population.
Although it is very attractive to use Bayesian statistics, naively applying Bayesian methods can be dangerous for three main reasons: the potential influence of priors, misinterpretation of Bayesian features and results, and improper reporting of Bayesian results. To deal with these three points of potential danger, we will spend a lot of time discussing these issues. You will learn how to use the WAMBS-checklist (When to worry and how to Avoid the Misuse of Bayesian Statistics). The purpose of the questionnaire is to describe 10 main points that should be thoroughly checked when applying Bayesian analysis.
During this course you will be gently introduced into Bayesian statistics. On each day, the morning session consists of lectures, and the afternoon session is a computer lab where the topics of the morning are applied on example data.
Instructor: Rens van de Schoot, Ph.D.
An associated professor in Methods and Statistics at Utrecht University, the Netherlands and extra-ordinary professor at North-West University in South Africa. He is a member of the Young Academy of the Royal Netherlands Academy of Arts and Sciences (KNAW). He is also associate editor of the European Journal of Developmental Psychology. He teaches many Bayesian and Mplus courses all around the world and he organizes Mplus users meetings in the Netherlands (mplus.fss.uu.nl).
Software and Computer Support
Participants should bring a laptop to the course. Before arriving, participants should acquire either the Mplus demo version 7 or higher or the full Mplus version 7 or higher. The computer exercises are based on the demo version, but there will be plenty of time to work on your own dataset and then the full version might be needed.
Also, JAGS, R, Blavaan and Rstudio should be installed on your laptop.
Specific instructions will be send before the course takes place.
In addition to a course packet, you will receive a zip file containing all course materials, including PowerPoint slides, exercises, data and scripts, output files, relevant supporting documentation, and recommended readings
If you are not familiar with the software Mplus or Blavaan in R you are requested to prepare an exercise in advance where you will also be shown how to get the software and how to run some very basic models. The exercise will be send to you after registration. This will save us some time on Day 1.
Knowledge of regression analysis is required. No previous knowledge of Bayesian analysis is assumed. You do not need to know matrix algebra, calculus, or likelihood theory. Since the course offers a gentle introduction there are hardly any formulas used in the lectures. The main focus is on conceptually understanding Bayesian statistics.
Participants from a variety of fields, including sociology, psychology, education, human development, marketing, business, biology, medicine, political science, and communication, can benefit from the course.