Statistics

An introduction to linear and non-linear predictive models, providing an interactive step-by-step guide to running these models, and key diagnostics using the R software platform.

This course will help build participants’ ability to work with data in R and undertake rigorous statistical analysis, including spatial analysis and linear regression, creating publication-standard graphs of the results. 

Survival analysis is used to find out the time it takes for an event of interest to occur (e.g., death, birth, recidivism). This course is designed as an introduction to survival analysis.

This 5-day intermediate course will help you choose from a range of statistical procedures to analyse your data. It aims to provide participants with an ability to understand, run and interpret these procedures.   

The focus of this course is on learning advanced statistical methods using R.

This masterclass offers a step-by-step, interactive introduction to R and RStudio for participants with no experience with these software packages.

A course for participants who have experience in qualitative or quantitative research and who are interested in applying QCA in their current or future studies.

This masterclass introduces students to choice models, discusses and contrasts the family of available model structures, and also talks about the data requirements, model estimation, and the use of models to produce outputs for policy. 

This 5-day course aims to provide you with the understanding and experience to undertake a basic research project in the social or health sciences using Stata as the statistical tool.

This 3-day course is designed to cover the fundamental principles of Structural Equation Modeling (SEM) and introduce the AMOS and/or Mplus software packages to estimate structural equation models.

This course is an introduction to time-series concepts and univariate time-series analysis. This course takes a practical applied approach (rather than mathematical), in running basic time-series analysis using Stata.

A hands-on introduction to applied choice modelling using the open-source Apollo software for the estimation of choice models. 

Designed as an applied introduction to SEM using Stata, this course aims to provide participants with a sound understanding of when to use SEM and how to assess and report their models.

This master-class has two objectives – to teach the design principles required to make plots that really have an impact on the reader, and to teach the practical skills required to create these plots within the R software package.

This masterclass introduces students to choice models, discusses and contrasts the family of available model structures, and covers both estimation on data and the use of models to produce outputs for policy. 

This course is designed as an introduction to mixed effects modelling. These models involve data arising from longitudinal studies or studies where the data exhibits some form of hierarchy, and sometimes referred to as multilevel modelling.

This course is designed as an advanced course in Structural Equation Modeling (SEM) for existing AMOS and/or Mplus users. 

This course covers enough of the statistical material for the intelligent use of multivariate statistical techniques. The approach is informal and applied rather than emphasising algebraic manipulation or proofs.

A step-by-step, interactive introduction to Python for participants with no experience with this software package. 

Designed as an applied introduction to SEM using Stata, this course aims to provide participants with a sound understanding of when to use SEM and how to assess and report their models

A 5-day introductory unit in statistical methods with the emphasis on statistical techniques applicable to the social sciences, although these introductory techniques are also relevant to the health sciences.

The aim of this course is to provide students with an accessible introduction to the fundamentals of quantitative political science research.

Multivariate analysis of variance (MANOVA) is an extension of analysis of variance (ANOVA) to deal with two or more continuous outcomes or dependent variables. This technique is used to determine whether multiple levels of independent variables on their own or in combination with one another have an effect on the outcome variables.

An introduction to supervised machine learning techniques for data science, providing an interactive step-by-step guide to running some of the standard statistical regression and classification machine learning models that every data scientist should know. 

This course covers enough of the statistical material for the intelligent use of multivariate statistical techniques. The approach is informal and applied rather than emphasising algebraic manipulation or proofs.

This course provides an overview of Longitudinal Data Analysis. As well as the statistical theory, an overview of the many applications and capabilities of LDA is given.

This course provides an overview of the fundamentals of SEM. As well as the statistical theory, an overview of the many applications and capabilities of SEM is given.