Background I went on a course in Cambridge over the summer of 2018. This was to get me up to speed on structural equation modelling (SEM), which has a lot of potential applications in scenarios where the pathways between measured and unmeasured variables are the central focus of the research question.
What is SEM? SEM is a mixture of confirmatory factor analysis (CFA) and path analysis. Another way to describe that, is that you have a measurement part and a structural part.
Background Longitudinal analysis is important as due to the temporal sequence of exposure then outcome, we can make a stronger case for causality. A derivative of a class of models that fit into the ‘data-mining’ family is sequence analysis. One use of this model is to understand lifetime states, e.g. being employed, being in education, being retired. By understanding these state sequences we can understand how the duration and timing of a state can affect health in the long term.
Background Group-based trajectory modelling (GBMT) is a way of identifying latent patterns of change from multiple individual trajectories. It is widely used within the field of economics and also becoming popular in health geography. The process of deciding the number of classes is not transparent and tends to be based on one or two model fit statistics. Klijn et al., created a fit-criteria assessment plot (F-CAP) that accepts universal data input to aid the decision on the number of classes (2017).