Introduction to Multilevel (hierarchical, or mixed effects) Models using R (online)
07/06/2023 - 08/06/2023
Nottingham Trent University
Mark Andrews, Associate Professor
Advanced (specialised prior knowledge)
Kelly Smith, Commercial Manager
Duration: 2 day online course
Course Module: Non-accredited
On this two-day course, you will obtain a practical and theoretical introduction to doing multilevel or mixed-effects regression modelling using R.
We will particularly focus on multilevel or mixed effects linear models, which are very widely used throughout the social and biological sciences.
We will use the popular `lme4` R package, as well as the acclaimed `brms` R package for Bayesian analyses.
This course is aimed at scientific researchers and data analysts who are interested in advancing their level of statistical knowledge and techniques beyond standard regression analysis to regression methods that are suitable for modelling clustered or hierarchical data sets, which are very common in the social and biological sciences.
You can sign up for this course at any point within the live period until 3 working days prior to the course commencing.
The course will cover these key topics:
- Random effects models. The defining feature of multilevel models is that they are models of models. We begin by using so-called random effects model to illustrate this. Here, we also cover the key concepts of statistical shrinkage and intraclass correlation.
- Linear mixed effects models. Next, we turn to multilevel linear models, also known as linear mixed effects models. We specifically deal with the cases of varying intercept and/or varying slope linear regression models.
- Multilevel linear models for nested data structures. We will consider multilevel linear models for nested, as in groups of groups, data. As an example, we will look at multilevel linear models applied to data from children within schools that are themselves within different cities, and where we model the variability of effects across the schools and across the cities.
- Multilevel linear models for crossed data structures. In some multilevel models, each observation occurs in multiple groups, but these groups are not nested. For example, children may be members of different schools and different social clubs, but the clubs are not subsets of schools, nor vice versa. These are known as crossed or multiclass data structures.
- Bayesian multilevel models. All of the models that we have considered can be handled, often more easily, using Bayesian models. Here, we provide an brief introduction to Bayesian models and how to perform examples of the models that we have considered using Bayesian methods and the `brms` R package.
During the course you’ll:
- gain a practical and theoretical introduction to multilevel and mixed effects regression models, with a particular focus on multilevel and mixed effects linear models.
- learn how to use the widely used `lme4` R package for multilevel and mixed effects regression.
- learn how to apply Bayesian approaches to multilevel modelling using the brms R package.
- learn how multilevel models are models of models and understand how random effects models serve as a solid basis for understanding multilevel and mixed effects models generally.
- explore the basic and general principles of multilevel and mixed effects linear models, focusing on varying intercept and / or varying slopes regression models
- further explore aspects of multilevel and mixed effects linear models, including multilevel models for nested and crossed data
- explore Bayesian approaches to multilevel and mixed effects linear models using the acclaimed `brms` software.
What will I gain?
By the end of the course, you’ll have acquired the know-how to model statistical data that is grouped, clustered, or hierarchically structured, and understand the general concepts of fixed, random, and mixed effects variables and how they are used in multilevel models.
You’ll have the capability to analyse complex and hierarchically arranged datasets using popular multilevel modelling software in the R language, compare competing models, and choose suitable model structures depending on the nature of the data.
On completion of at least 80% of the course, you’ll receive a certificate of attendance.
Where you'll learn:?The course is delivered through interactive online workshops via Zoom.
It will be largely practical, hands-on, and workshop based.
For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories.
Throughout the course, we will use real-world data sets and coding examples.
Tutor Profile: Mark Andrews is an Associate Professor at Nottingham Trent University whose research and teaching is focused on statistical methodology in research in the social and biological sciences.
He is the author of 2021 textbook on data science using R that is aimed at scientific researchers, and has a forthcoming new textbook on statistics and data science that is aimed at undergraduates in science courses.
His background is in computational cognitive science and mathematical psychology.
Any questions? Contact email@example.com, Commercial Manager, School of Social Sciences
Other available online CPD courses in this series include
Introduction to statistics using R and Rstudio
Introduction to Data Wrangling using R and tidyverse
Introduction to Data Visualization with R using ggplot
Introduction to Generalised Linear Models in R
Introduction to Bayesian Data Analysis with R
£360.00, fee includes VAT
Website and registration:
Frameworks for Research and Research Designs, Data Collection, Data Quality and Data Management , Quantitative Data Handling and Data Analysis, ICT and Software, Research Skills, Communication and Dissemination, RStats, multilevel or mixed-effects regression modelling, `lme4` R package, `brms` R package, Bayesian analyses, regression analysis, regression methods, clustered modelling, hierarchical data set, random effects, linear effects, multilevel effects, Baye
Related publications and presentations:
Frameworks for Research and Research Designs
Data Quality and Data Management
Quantitative Data Handling and Data Analysis
ICT and Software
Research Skills, Communication and Dissemination