Introduction to Mixed Models
On-demand introduction to linear and generalised mixed models in R. Covers multilevel modelling, nested and crossed designs, random effects, and Bayesian approaches.
Duration: 12 Hours
Format: Recorded ‘on-demand’ Format
£250Registration Fee
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Course Description
This course provides a comprehensive practical and theoretical introduction to multilevel models, also known as hierarchical or mixed effects models. We will focus primarily on multilevel linear models, but also cover multilevel generalized linear models. Likewise, we will also describe Bayesian approaches to multilevel modelling. We will begin by focusing on random effects multilevel models. These models make it clear how multilevel models are in fact models of models. In addition, random effects models serve as a solid basis for understanding mixed effects, i.e. fixed and random effects, models. In this coverage of random effects, we will also cover the important concepts of statistical shrinkage in the estimation of effects, as well as intraclass correlation. We then proceed to cover linear mixed effects models, particularly focusing on varying intercept and/or varying slopes regression models. We will then cover further aspects of linear mixed effects models, including multilevel models for nested and crossed data data, and group level predictor variables. Towards the end of the course we also cover generalized linear mixed models (GLMMs), how to accommodate overdispersion through individual-level random effects, as well as Bayesian approaches to multilevel levels using the brms R package.
What You’ll Learn
During the course will cover the following:
- Random effects models.
- Normal random effects models.
- Linear mixed effects models.
- Multilevel models for nested data.
- Multilevel models for crossed data.
- Group level predictors.
- Generalized linear mixed models (GLMMs).
- Bayesian multilevel models.
Course Format
Flexible Learning Structure
Learn through a carefully structured mix of lecture recordings and guided exercises that you can pause, revisit, and complete at your own pace—ideal for busy professionals or those balancing multiple commitments.
Access Anytime, Anywhere
All course content is available on-demand, making it accessible across all time zones without the need to attend live sessions or adjust your schedule.
Independent Exploration with Support
Engage deeply with course topics through self-directed study, with the option to reach out to instructors via email for clarification or deeper discussion.
Comprehensive Learning Resources
Gain full access to the same high-quality materials provided in live sessions, including code, datasets, and presentation slides—all available to download and keep. Please note recordings can only be streamed.
Work With Your Own Data, On Your Terms
Apply what you learn directly to your own data projects as you go, allowing for a personalized and immediately practical learning experience.
Continued Guidance and Resource Access
Receive 30 days of post-enrolment email support and unrestricted access to all session recordings during that time, so you can review and reinforce your learning as needed.
Who Should Attend / Intended Audiences
This course is intended for anyone interested in using R for data science or statistical analysis. R is a versatile programming language widely adopted across academic research, as well as in both the public and private sectors. The course assumes a basic understanding of general statistical concepts, including linear models and statistical inference (such as p-values and confidence intervals). While no advanced programming experience is required, participants should have some prior exposure to R and RStudio — including familiarity with basic R syntax and commands, writing code in the console and script editor, and loading data from external files.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
All necessary R packages will be introduced and installed during the workshop.
Dr. Rafael De Andrade Moral
Rafael is a statistician working at the intersection of ecological science, environmental research, and applied statistical modelling. His work focuses on developing and applying statistical and mathematical tools to understand ecological dynamics, improve wildlife management strategies, and support sustainable agricultural and environmental practices. With a strong foundation in both biology and statistics, Rafael’s research spans areas such as hierarchical modelling, population dynamics, and the integration of ecological theory with real-world data.
Rafael holds a PhD in Statistics from the University of São Paulo, building on an undergraduate background in Biology. He is currently an Associate Professor of Statistics at Maynooth University, Ireland, where he also leads the Theoretical and Statistical Ecology Group — a multidisciplinary research hub dedicated to advancing quantitative ecology.
In addition to his academic work, Rafael is deeply invested in science communication and innovative teaching. He produces educational music videos and statistical parodies, using creative media to make statistical concepts more engaging and accessible to students and the public alike.
Education & Career
• PhD in Statistics – University of São Paulo
• BSc in Biology
• Associate Professor of Statistics – Maynooth University
• Director – Theoretical and Statistical Ecology Group
Research Focus
Rafael’s research is rooted in ecological and environmental statistics, particularly:
• Statistical modelling of species distributions and abundance
• Applications of Bayesian and hierarchical models in wildlife and agricultural contexts
• Integrative approaches combining field data, simulation, and theory to inform policy and conservation
• Methodological innovation in data-poor or complex ecological systems
Current Projects
• Statistical methods for population modelling and biodiversity monitoring
• Quantitative frameworks for wildlife management under uncertainty
• Modelling ecological responses to climate and land-use changes
• Public outreach through creative science communication in Statistics
Professional Activites
Rafael collaborates widely with ecologists, conservationists, and agricultural scientists, providing expert statistical input on study design, modelling, and data analysis. He also supervises postgraduate research across interdisciplinary projects in quantitative ecology.
Teaching & Skills
• Teaches courses in statistical modelling, environmental statistics, and data analysis in R
• Promotes engaging and inclusive teaching practices, including music-based educational content
• Advocates for open science, reproducibility, and the integration of theory with application
Links
• ResearchGate
• Google Scholar
• ORCID
• GitHub
Session 1 – 02:00:00 – Random effects models.
The defining feature of multilevel models is that they are models of models. We begin by using a binomial random effects model to illustrate this. Specifically, we show how multilevel models are models of the variability in models of different clusters or groups of data.
Session 2 – 02:00:00 – Normal random effects models.
Normal, as in normal distribution, random effects models are the key to understanding the more general and widely used linear mixed effects models. Here, we also cover the key concepts of statistical shrinkage and intraclass correlation.
Session 3 – 01:20:00 – 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.
Session 4 – 01:20:00 – Multilevel models for nested data.
Here, 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 students within classes that are themselves within different schools, and where we model the variability of effects across the classes and across the schools.
Session 5 – 01:20:00 – Multilevel models for crossed data.
In some multilevel models, each observation occurs in multiple groups, but these groups are not nested. For example, animals may be members of different species and in different locations, but the species are not subsets of locations, nor vice versa. These are known as crossed or multiclass data structures.
Session 6 – 01:20:00 – Group level predictors.
In some multilevel regression models, predictor variable are sometimes associated with individuals, and sometimes associated with their groups. In this section, we consider how to handle these two situations.
Session 7 – 01:20:00 – Generalized linear mixed models (GLMMs).
Here, we extend the linear mixed model to the exponential family of distributions and showcase an example using the Poisson GLMM. We also cover how to accommodate overdispersion through individual-level random effects.
Session 8 – 01:20:00 – 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.
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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