Introduction to Latent Growth Models using R (ONLINE) (join a waiting list)

Description:

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 Latent Growth Models using the lavaan package in R. In addition to the fact that the package is free and open source it 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;

• Latent Growth Models;

The course will be a combination of lecturing and practicals using real world data.

The exercises and practicals will be shared with the students in the day of the lecture.

Prerequisites

Good knowledge of regression modelling and at least basic knowledge of R

Pre course information 

In advance you will receive a recommended reading list.

You can find out more about the teacher at: www.alexcernat.com

All the exercises will be in R. Please install the latest version of R https://cran.r-project.org/ and latest version of Rstudio  before the class.

Also install in advance the lavaan (install.packages("lavaan", dep =TRUE)) and the tidyverse (install.packages("tidyverse", dep =TRUE)) packages.

The course will be delivered online via Zoom.

Cost:

For UK registered postgraduate students £30.00

For staff at UK academic institutions, ESRC funded researchers and registered charity organisations
£60.00

For all other participants £100

Website and registration:

https://go.soton.ac.uk/cik

Region:

North West

Keywords:

Latent Variable Models, Quantitative Software, R

Related publications and presentations:

Latent Variable Models
Quantitative Software
R

Back to archive...