Introduction to Latent Growth Models using R (join a waiting list)
The University of Manchester
Dr Alexandru Cernat
Entry (no or almost no prior knowledge)
Claire Spencer, firstname.lastname@example.org
Longitudinal data (data collected multiple times from the same cases) is becoming increasingly popular due to the important insights it can bring us.Structural Equation Modelling (SEM) offers a flexible framework in which longitudinal data can be analysed.
The course will be delivered online 10am - 5pm (BST) and cover some of the basics and more advanced Latent Growth Models using lavaan package in R.
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.
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 fee per teaching day is: • £30 per day for registered students • £60 per day for staff at academic institutions, Research Councils researchers, public sector staff and staff at registered charity organisations and recognised research institutions. • £100 per day for all other participants In the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. No refunds are available after this date. If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given. NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of the cancellation of a course. The University of Southampton’s Online Store T&Cs also continue to apply.
Website and registration:
Longitudinal Data Analysis, Latent class growth analysis, Quantitative Software, R
Related publications and presentations: