Introduction to Latent Growth Models using R (ONLINE) (join a waiting list)
Date:
25/09/2020
Organised by:
The University of Manchester
Presenter:
Dr Alexandru Cernat
Level:
Intermediate (some prior knowledge)
Contact:
Claire Spencer, claire.spencer@manchester.ac.uk
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.
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 from 10am to 5pm. Partcipants areexpected to have a good knowledge of regression modelling and at least basic knowledge of R
Cost:
• £30 per day for UK/EU registered students
• £60 per day for staff at UK/EU academic institutions, UK/EU Research Councils researchers, UK/EU public sector staff, staff at UK/EU registered charity organisations and recognised UK/EU 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:
Region:
North West
Keywords:
Quantitative Data Handling and Data Analysis
Related publications and presentations:
Quantitative Data Handling and Data Analysis