Structural equation modelling (SEM) is carried out with the aim of testing hypotheses
on the model of the researcher in a quantitative way, using the sampled data. Although
SEM has developed in many aspects over the past few decades, there are still numerous
advances which can make SEM an even more powerful technique. We propose representing
the nal theoretical SEM by a Bayesian Network (BN), which we would like to call a
Probabilistic Structural Equation Model (PSEM). With the PSEM, we can take things
a step further and conduct inference by explicitly entering evidence into the network and
performing di erent types of inferences. Because the direction of the inference is not an
issue, various scenarios can be simulated using the BN. The augmentation of SEM with
BN provides signi cant contributions to the eld. Firstly, structural learning can mine
data for additional causal information which is not necessarily clear when hypothesising
causality from theory. Secondly, the inference ability of the BN provides not only insight
as mentioned before, but acts as an interactive tool as the `what-if' analysis is dynamic.
Mini Dissertation (MCom)--University of Pretoria, 2018.