Live online course · February 2027
Introduction to Longitudinal Data Analysis using R
Choose, fit and interpret the right longitudinal model for your research
A live online course with Prof. Alexandru Cernat, author of Longitudinal Data Analysis Using R. Learn how to use longitudinal analysis to answer your research questions.
- Dates1–12 Feb 2027
- Time08:00–11:00 Pacific
11:00–14:00 Eastern
16:00–19:00 UK - FormatLive on Zoom, 10 × 3 hours
- PriceUS$1,200, 20 places
Full refund within 14 days of booking or after the first session if it’s not for you
Paying through your university or employer? Request an institutional invoice
As a PhD researcher, this course helped me understand the various topics involved in longitudinal research and gave me the confidence to use R for analysis. I'd highly recommend it to any Early Career Researcher looking to build these skills!
Vanessa Cieplinska, UCL, PhD Candidate
★★★★★
About the course
Longitudinal data is essential for social and health research because it allows us to understand how people, organisations, and societies change, why events happen, and to support stronger causal inference.
In this course, you will learn both how to clean longitudinal data and the main statistical models used to analyse it. The course will cover three fundamental frameworks for analysing longitudinal data: multilevel modelling, structural equation modelling and event history analysis.
The course is organised as a mixture of lectures and hands-on practicals using real-world data. During the course, there will also be opportunities to discuss how to apply these models in your own research.
The course runs over two weeks, three hours a day, so you can complete the guided reading for each session and apply the techniques we cover to your own research. This learn-apply-review format gives you time to consolidate each method, use it with your own data, and resolve problems while support is still available. The follow-up data clinics will further help you apply these methods to your own research.
By the end of the course, you will be able to
- Prepare and visualise longitudinal data in R
- Choose between multilevel, SEM and event-history models
- Fit and interpret the principal longitudinal approaches
- Recognise their assumptions and limitations
- Develop a defensible analysis plan for your own research
Schedule
Ten live sessions · 08:00–11:00 Pacific · 11:00–14:00 Eastern · 16:00–19:00 UK
Week 1 · 1–5 February
- Mon 1 FebLongitudinal data preparation
- Tue 2 FebLongitudinal data visualisation
- Wed 3 FebIntroduction to SEM and autoregressive models
- Thu 4 FebCross-lagged models and longitudinal mediation
- Fri 5 FebFixed effects and the multilevel model for change
Week 2 · 8–12 February
- Mon 8 FebAdvanced multilevel models for change
- Tue 9 FebIntroduction to Latent Growth Modelling
- Wed 10 FebAdvanced Latent Growth Modelling
- Thu 11 FebLongitudinal equivalence and second-order LGM
- Fri 12 FebSurvival analysis in discrete and continuous time
All sessions are recorded (available for 30 days).
What's included
Before the course
- Introduction to R for Social Researchers - full on-demand course (RRP $250)
- Mediation and Moderation Analysis using R - full on-demand course (RRP $200)
During the course
- 10 live 3-hour sessions: live lectures + guided R practicals, support with your research and 30 days recording of class
- Longitudinal Data Analysis Using R - the complete ebook (RRP $80) and recommended reading for each day
- The Ultimate Longitudinal Analysis Cheat Sheet (RRP $20)
After the course
- Three live group data clinics (RRP $400) after the course
This course provided me with the confidence to tackle large longitudinal datasets for the first time, giving a helpful and thorough overview of the primary methods in the field. In particular, the guided practicals and associated R code were extremely helpful in ensuring I understood how to apply these methods, and gave me the skills to use them in my own datasets.
Jessica Foley, Postdoctoral Scholar, Tufts University
Your instructor
I'm Alexandru Cernat, Professor in Methodology and Social Data Science at the University of Manchester. I have more than 10 years of experience doing research and teaching statistical methods, and hundreds of students from around the world have taken my classes. I'm the editor of Measurement Error in Longitudinal Data and the author of Longitudinal Data Analysis Using R. I've published over 50 peer-reviewed articles in top journals and have served as an associate editor for the Journal of the Royal Statistical Society A, Journal of Survey Statistics and Methodology and Survey Research Methods. Thousands of researchers use my guides at longitudinalanalysis.com every month to learn how to analyse their data.
What participants say
This course was very comprehensive in scope and depth. I feel much more knowledgeable about different methods and models in longitudinal data analysis (including their strengths and weaknesses). The combination of having access to the book, listening to the lectures, and doing the practical exercises helped to acquire the knowledge in an efficient way.
P. Linh Nguyen, INED/University of Mannheim
Prof. Dr. Cernat is able to teach in a clear, engaging, and pedagogically effective way, regardless the prior knowledge and level of expertise of his students. He always provides clear answers to questions, no matter how specific or complex they are.
Emir Zecovic, Federal Institute for Population Research (BiB), PhD Student
I owe Alex’s 2018 course my ‘re-birth’ as an R data manager and analyst.
Teresio Poggio, Research Assistant, University of Trento
The course truly helped me develop a more systematic understanding of the longitudinal data analysis workflow and consolidate my previously fragmented experience with data analysis. I would recommend it to researchers seeking a clear, well-structured framework for planning and conducting longitudinal analyses.
Jingjing Jiang, PhD student, Queen Mary, University of London
The course provided a clear, structured overview of the different research models and the steps to follow in the correct order. I especially appreciated the opportunity to discuss my own research questions, with time for personalized feedback and guidance.
Ans De Wachter
Thoroughly recommend anything taught by Alex.
Michelle Degli Esposti, Postdoctoral Researcher, University of Oxford
Frequently asked questions
What are the prerequisites for this course?
Basic knowledge of regression and R. Need to learn these? No worries, use the book and the on-demand courses included to get ready!
Can't make every session?
Recordings of the lectures, solutions, and data clinics are available for 30 days from the date of recording.
Software used?
R and RStudio (free). Need to learn it? No problem; use the included course to get ready.
When do I get access to the bonus content?
You will get the bonuses immediately on enrolment.
What is the cancellation/refund policy?
Full refund within 14 days of booking, no questions asked. And if you attend the first session and find it's not for you, email me that week, and I'll refund you in full.
Can my institution pay by invoice?
Yes. If your university or employer needs to pay by invoice or purchase order, contact me with the participant's name and email, your institution's billing address and any purchase order number, and I will send you an invoice.
Is there a certificate?
Yes, you will receive a certificate at the end of the course.
How do the data clinics work?
You will submit questions about your own research in advance of the meeting. During the live online session, I will answer all the questions received. Meeting recordings will be available for 30 days from the date of recording.
Which time zone applies?
Sessions run 11:00–14:00 Eastern (08:00–11:00 Pacific, 16:00–19:00 UK).
If you have any other questions, feel free to contact me
Join the February cohort
Ten live sessions on Zoom · 1–12 February 2027 · US$1,200 · 20 places
Paying through your institution? Request an invoice