(Online) SEM in R workshop (3.5 or 5 days)

Organised by

University of Cambridge


Dr Luning Sun and Dr Aiden Loe


19/04/2021 - 23/04/2021


Judge Business School, Trumpington St, Cambridge


View in Google Maps  (CB2 1AG)


+44 (0) 1223 769483


This live online course (administered via Zoom) offers an engaging introduction to Structural Equation Modelling (SEM) using R, the popular open-source software for statistical computing and graphics. The course will run over 5 consecutive days and will combine synchronous live lectures, individual exercises, group discussions and small group supervisions to simulate a live classroom environment. High contact with our expert instructors and interactive group work will further contribute to recreating the classic Cambridge experience in an online setting.

This course will present the lavaan package, rapidly becoming the tool of preference for SEM in R. Participants will actively work through practical examples to gain first-hand experience in the application of factor analysis and other more advanced latent trait models. We will also introduce ggplot2, a simple R package for data visualisation. You will learn the following topics in this course:  

  • Introduction to R and data analysis
  • Graphical representations of data points and latent trait models
  • Basic concepts of factor analysis (EFA/CFA/Categorical CFA)
  • Basic and advanced Structural Equation Models (SEM)
  • Application of mediation & moderation techniques and bootstrapping estimator
  • Multiple-group analysis and evaluation of continuous and categorical data measurement invariance, as well as longitudinal measurement invariance
  • Latent growth curve modelling

You don’t need to know R to follow the course. However, if you are not familiar with R, you will need to choose the 5 day option, which includes an introduction to the R software on Day 1. On completion, participants should have a good knowledge of the topics covered and have acquired an independent use of R and latent trait analysis.

We believe in active learning and developing practical skills. Thus, the necessary theoretical introduction will be illustrated with practical examples and we will be working with real data. No prior knowledge about SEM is assumed. The pace of teaching is adjusted to suit the level of the participants. Teaching will be in small groups so that delegates can make the most of the teaching.


Entry (no or almost no prior knowledge)



Website and registration


East of England


Growth curve models, Latent trait analysis, Principal components analysis, Factor analysis, Confirmatory factor analysis, Structural equation models

Related publications and presentations

Growth curve models
Latent trait analysis
Principal components analysis
Factor analysis
Confirmatory factor analysis
Structural equation models

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