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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:
By the end of the course participants will:
The course is aimed at people from all disciplines and types of institutions that want to learn about longitudinal data analysis or about latent variable modelling. Pre-requisites Knowledge of regression analysis. Prior knowledge of R or SEM would be an advantage but not essential. Preparatory Reading For an introduction to SEM: Brown, T. (2006). Confirmatory Factor Analysis for Applied Research (1st ed.). The Guilford Press. Using lavaan in R: Beaujean, A. A. (2014). Latent Variable Modeling Using R: A Step-by-Step Guide. New York: Routledge. For Longitudinal SEM: Little, T. D. (2013). Longitudinal Structural Equation Modeling. New York: Guilford Press. |