Course Overview: This course will cover introductory mixed or hierarchical modelling (fixed and random effects models) for real-world data sets from both a Frequentist and Bayesian perspective. These methods lie at the forefront of statistics research and are a vital tool in the scientist's toolbox. The course focuses on introducing concepts and demonstrating good practice in mixed modelling. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using both the standard lme4…

Course Overview: This course provides a general introduction to Bayesian data analysis using R and the Bayesian probabilistic programming language Stan. We begin with a gentle introduction to all the fundamental principles and concepts of Bayesian data analysis: the likelihood function, prior distributions, posterior distributions, high posterior density intervals, posterior predictive distributions, marginal likelihoods, Bayes factors, etc. We will do this using some simple probabilistic models that are easy to understand and easy to work with. We then proceed to…

Course Overview: This course aims to help you to draw better statistical inferences from empirical research, improve the statistical questions you ask when you collect data, design better and more efficient studies, and improve your meta-analytic thinking. In practical, hands on assignments, you will learn techniques and tools that can be immediately implemented in your own research, such as thinking about the smallest effect size you are interested in, justifying your sample size, evaluate findings in the literature while keeping…