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Introduction to Longitudinal Structural Equation Modelling with R

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Course Information

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Longitudinal data (data collected multiple times from the same cases) is becoming increasingly popular due to the important insights it can bring us. For example, it can be used to track how individuals change in time and what are the causes of change, it can also be used to understand causal relationships or used as part of impact evaluation. Unfortunately, traditional models such as OLS regression are not appropriate as multiple individuals are nested in different time points. For this reason specialised statistical models need to be learned.


Structural Equation Modelling (SEM) offers a flexible framework in which longitudinal data can be analysed. It offers a series of advantages compared to other approaches such as traditional multilevel models: the inclusion of multiple relationships (path analysis, mediation, etc.), the inclusion of measurement error, the estimation of change in measurement error, multi-group analysis, etc.


The course will cover some of the basics and more advanced models used in Longitudinal SEM using the lavaan package in R. In addition to the fact that the package is free and open source they also offer great flexibility, being able to estimate most of the models typically used in Longitudinal SEM.


The course covers:

  • Introduction to R and lavaan package;
  • Short discussion of the SEM framework;
  • Regression and path analysis in SEM;
  • Cross-lagged models;
  • Latent Growth Models;
  • Factor models and their identification;
  • Equivalence testing;
  • Second order cross-lagged and Latent Growth Models;


By the end of the course participants will:

  • Know what is SEM;
  • Be able to estimate and interpret results from a cross-lagged model;
  • Be able to estimate and interpret results from a Latent Growth Model;
  • Be able to estimate and interpret longitudinal equivalence testing;
  • Understand second order factors and how they can be used in Longitudinal SEM;



Intermediate level course.

Knowledge of regression analysis. Prior knowledge of R or SEM would be an advantage but not essential.


Preparatory Reading


For an introduction to Latent Class Analysis:

Enders, C. K. (2010). Applied Missing Data Analysis (1st ed.). New York: The Guilford Press.


Further reading


Using lavaan in R:

Beaujean, A. A. (2014). Latent Variable Modeling Using R: A Step-by-Step Guide. New York: Routledge.


Course Code


Course Dates

18th January 2018 – 19th January 2018

Places Available


Course Leader

Dr Alexandru Cernat

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