Bayesian structural equation modelling: methods and applications

Abstract

Structural equation models (SEMs) are commonly used in social and behavioural sciences to study the structural relationships between observed variables and latent constructs or unobserved variables. Recently, Bayesian fitting procedures for SEMs have received more attention, as they overcome the issues of frequentist approaches when the number of observations is small and facilitate the adoption of more flexible model structures. In this talk, I will present an application of Bayesian SEMs in analysing the nature of the dose-response relation between prenatal alcohol exposure and child cognition, based on data from multiple cohorts. I will then introduce a framework for fitting Bayesian SEMs via variational approximations, that provides a fast and reliable alternative to Markov chain Monte Carlo.