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…

Course Overview: Python is one of the most widely used and highly valued programming languages in the world, and is especially widely used in data science, machine learning, and in other scientific computing applications. This course provides both a general introduction to programming with Python and a comprehensive introduction to using Python for data science, machine learning, and scientific computing. The major topics that we will cover include the following: the fundamentals of general purpose programming in Python; using Jupyter…

Course Overview: Survival analysis is a set of statistical methods initially designed to analyse data giving the times at which individuals die, and assess the effect that different predictor variables have on the rate of death. However, its applications are much broader than this: it can be used to analyse any time-to-event data. Ecologists and evolutionary biologists often encounter data of this kind. Often factors influencing survival itself will be of interest. But there are many other cases, e.g. what…

Course Overview: This 5-day course will involve a combination of lectures and practical sessions. Students will learn to build and fit custom models for analysing behavioural data using maximum likelihood techniques in R. This flexible approach allows a researcher to a) use a statistical model that directly represents their hypothesis, in cases where standard models are not appropriate and b) better understand how standard statistical models (e.g. GLMs) are fitted, many of which are fitted by maximum likelihood. Students will…

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…