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…

Course Overview: This course will cover introductory hierarchical modelling for real-world data sets from a 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 hierarchical models. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using the Bayesian modelling software Jags and Stan through the R…

Course Overview: This course provides a general introduction to nonlinear regression analysis, covering major topics including, but not limited to, general and generalized linear models, generalized additive models, spline and radial basis function regression, and Gaussian process regression. We approach the general topic of nonlinear regression by showing how the powerful and flexible statistical modelling framework of general and generalized linear models, and their multilevel counterparts, can be extended to handle nonlinear relationships between predictor and outcome variables. We begin…

Course Overview: This course provides a comprehensive introduction to a set of inter-related topics of widespread applicability in the social social sciences: structural equation modelling, path analysis, causal modelling, mediation analysis, latent variable modelling (including factor analysis and latent class analysis), Bayesian networks, graphical models, and other related topics. The course begins with a thorough review, both practical and theoretical, of regression modelling, particularly on general and generalized linear regression. We then turn to the topic of path analysis. At…