Introduction to Multilevel and Mixed Effects Models using R - Online
- Level(s) of Study: Short course; Professional
- Course Fee:
£360
- Start Date(s): Thursday 5 June 2025
- Duration: Two days (Thursday and Friday) 9.30 am - 5.30 pm
- Study Mode(s): Part-time
- Entry Requirements: More information
Introduction:
Learn how to analyse hierarchical data structures using R.
R is a major tool in modern data analysis and statistics, used extensively in academic research as well as by data analysts in the public and private sectors. Its flexibility, comprehensive ecosystem of packages, and active community have made it highly suited for all aspects of data analysis.
This two-day online course offers a thorough, hands-on introduction to working with R and RStudio, which is the most widely used integrated development environment for R. By participating, you’ll gain the foundational skills needed to handle real-world datasets, develop reproducible analytical workflows, create effective data visualisations, and conduct a wide range of common statistical techniques.
Whether you’re an early-career researcher, an academic looking to broaden your methods, or a professional data analyst interested in robust statistical tooling, this course equips you to move confidently toward more advanced analysis.
Over the two days you'll gain expertise in:
-
Random effects models – accounting for variation across groups. -
Mixed effects models – combining fixed and random effects for better predictions. -
Hierarchical and crossed data – structuring models for nested and overlapping groups. -
Explained variance and power analysis – ensuring models are robust and meaningful.
What you’ll study
Through real-world examples and coding exercises, you’ll develop the skills to fit, interpret, and evaluate multilevel models with confidence. By the end of the course, you’ll be equipped to handle complex, structured data and make informed statistical decisions in research and industry.
Topic 1: Random Effects Models
- Understand how multilevel models handle variability across groups.
- Explore statistical shrinkage and intraclass correlation.
Topic 2: Normal Random Effects Models
- Learn how normal random effects models bridge to linear mixed effects models.
- Develop insights into hierarchical data structures.
Topic 3: Linear Mixed Effects Models
- Implement varying intercept and varying slope models.
- Gain practical experience fitting and interpreting these models in R.
Topic 4: Multilevel Models for Nested Data
- Learn to model hierarchical data structures
- Understand how nesting affects statistical assumptions and model fit.
Topic 5: Multilevel Models for Crossed Data
- Handle datasets where groups overlap in complex ways
- Compare and contrast nested vs. crossed data structures.
Topic 6: Group-Level Predictors
- Incorporate and interpret predictors that vary at the group level.
- Learn best practices for handling multilevel covariates.
Topic 7: Explained Variance in Multilevel Models
- Use R-squared and related metrics to evaluate model fit.
- Understand how multilevel models partition variance between levels.
Topic 8: Power Analysis and Sample Size Determination
- Learn how to perform power analysis for multilevel studies.
- Ensure your sample size is sufficient for reliable inference.
How you’re taught
This course is designed to be highly interactive, combining:
- Live online sessions via Zoom.
- Hands-on coding workshops with real-world datasets.
- Expert-led discussions to deepen your statistical reasoning.
- Downloadable resources including code, datasets, and exercises.
Contact hours
6 hours per day, plus two 1-hour breaks.
Session 1: 9:30 am - 11:30 am
Session 2: 12:30 pm - 2:30 pm
Session 3: 3:30 pm - 5:30 pm
Careers and employability
Certificate of attendance and digital badge
Upon successful completion of the course, you will receive a digital certificate of attendance and a digital badge powered by .
Your is more than just a certificate – it’s secure, verifiable, and protected against fraud through encryption and blockchain technology.
They also come with detailed metadata, including an overview of the skills you have achieved on the course, evidence of completion, and assessment criteria if appropriate.
Share your achievements seamlessly with friends, customers, and potential employers online, and proudly add your badge or certificate to social media platforms such as LinkedIn, so all the right people can see it.
Entry requirements
This course is aimed at:
Researchers and Analysts who have experience with regression models and want to extend their statistical toolkit.
Academics and PhD Students working with hierarchical data structures.
Data Scientists and Statisticians looking to refine their multilevel modelling techniques.
Professionals in government, healthcare, and social sciences who work with structured data.
Prerequisites
- Familiarity with R is assumed.
- For an introduction to R, consider our Introduction to Statistics using R and RStudio course.
Fees and funding
How to apply
Ready to Elevate Your Data Skills?
Any questions?
Contact the short course team:
Email: SOCCommercial@ntu.ac.uk
Tel: +44 (0)115 848 4083