Training and Events
Introduction to Latent Growth Models using R (ONLINE)
|The University of Manchester|
Dr Alexandru Cernat
View in Google Maps (M19 6PL)
Claire Spencer, email@example.com
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:
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.
Intermediate (some prior knowledge)
• £30 per day for UK/EU registered students
Website and registration
Quantitative Data Handling and Data Analysis
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