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Introduction to Bayesian hierarchical modelling using R (IBHM03)
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…
Find out more »Generalised Linear (MIXED) (GLMM), Nonlinear (NLGLM) And General Additive Models (MIXED) (GAMM) (GNAM01)
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…
Find out more »Structural Equation Models, Path Analysis, Causal Modelling and Latent Variable Models Using R
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…
Find out more »Data Science/Analytics using Python (DSAP01)
Course Overview: Coming soon... Intended Audience Coming soon... Venue - PS statistics head office, 53 Morrison Street, Glasgow, G5 8LB - Google map Availability - 30 places Duration – 5 days Contact hours – Approx. 28 hours ECT’s – Equal to 3 ECT’s Language - English Packages We offer COURSE ONLY and ACCOMMODATION PACKAGES; • COURSE ONLY – Includes lunch and refreshments and welcome meal Monday evening. • ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch,…
Find out more »Introduction to Behavioural data analysis using R (IBDA01)
Course Overview: This course aims to provide participants with the skills needed to analyse a wide range of behavioural data in the R statistical environment. We will focus on the linear model and the various ways it can be extended to accommodate the different kinds of data commonly encountered in behavioural research. Participants will learn how to model continuous, binary, count (including zero-inflated) and time-to-event data and how to include and interpret continuous and categorical predictors, interactions and random effects.…
Find out more »Behavioural data analysis using maximum likelihood in R (BDML02)
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…
Find out more »Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan (BDRS02)
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…
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